US business location data: search, sample, count, query rows or buy CSVs (Stripe or x402 USDC).
Scored across 16 tools
Most tools are clearly distinguishable by resource+action (search_datasets vs get_dataset vs get_sample, and a clean 2x2 of {whole file, filtered rows} x {crypto, card} across buy_dataset/create_checkout/query_locations/create_query_checkout). However, the three free comparison tools — cotenancy, count_by_area, and relate_locations — genuinely overlap on 'how do these two sets of places relate,' and their purposes are only separated by long prose examples rather than crisp boundaries.
Fifteen of sixteen names follow a clean verb_noun pattern (get_dataset, count_locations, create_checkout, search_datasets, send_feedback, etc.). The lone exception is 'cotenancy,' a bare noun that breaks the convention, and 'email_quote'/'check_order' are slightly loose verbs, but overall the scheme is predictable.
Sixteen tools is on the heavier side but justifiable for a paid data marketplace spanning discovery, free counting/preview, pricing, two payment rails, and comparison analytics. Each tool earns its place; it sits at the top edge of the comfortable 3-15 band but not excessively so.
The surface covers the full lifecycle: discovery (search_datasets), inspection (get_dataset, get_sample), free analysis (count_locations, count_by_area, cotenancy, relate_locations), pricing (get_quote, count_locations quote), purchase on both crypto and card rails, order verification (check_order), and outbound actions (email_quote, request_list, send_feedback). No obvious dead ends for the domain.
Buy an ENTIRE dataset outright and get a permanent download link for the CSV. Pays once in USDC on Base, at the same list price a human pays by card — no account and no checkout page.\n\nPrefer this over repeated query_locations calls whenever you want most of a file. Metered queries are priced per row and deliberately cost more than the file if you assemble it that way, so past a few hundred rows buying outright is both cheaper and complete. get_dataset (free) gives the price and record count first.
| Name | Required | Description | Default |
|---|---|---|---|
| dataset | Yes | Dataset slug, e.g. generac-dealers. Use search_datasets first. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations only declare the mutation/open-world flags; the description adds the substantive behavior: single USDC payment on Base, same list price as card, no account or checkout, and a permanent download link. It stops short of describing failure/refund behavior or payment-confirmation timing, which would be the remaining gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short paragraphs, the action and outcome front-loaded, followed by the decision rule. Every sentence carries either purpose, cost logic, or a pointer to a sibling tool; no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter purchase tool with no output schema, the description covers what happens (permanent CSV download), cost model, and prerequisites (get_dataset for pricing). Nothing an agent needs to invoke it correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single 'dataset' parameter, so the schema already carries the semantics (slug, example, 'use search_datasets first'). The description adds no parameter-level detail, making the baseline 3 appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource ('Buy an ENTIRE dataset outright') plus the outcome ('permanent download link for the CSV'). It explicitly distinguishes itself from query_locations and get_dataset, so an agent can pick it without opening a schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Gives an explicit when-to-use rule ('prefer this over repeated query_locations calls whenever you want most of a file'), quantifies the crossover ('past a few hundred rows'), and routes the agent to get_dataset for price/record count first. Nothing is left to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Given a Stripe Checkout session id (cs_…), reports whether it is paid and, if so, returns the permanent download link for the CSV. Works for whole files, filtered rows and combined (several-dataset) orders. Any download link takes ?shape=hubspot or ?shape=salesforce for CRM-ready column names (every column kept).
| Name | Required | Description | Default |
|---|---|---|---|
| sessionId | Yes | Checkout session id from create_checkout |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, non-destructive and open-world. The description adds genuinely useful behavior beyond that: the link is permanent, it covers whole/filtered/combined orders, and links accept a ?shape= parameter. It doesn't say what is returned for an unpaid session or whether the result can change, which keeps it from a 5.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three front-loaded sentences with almost no waste: purpose first, then order-type scope, then the shape option. The CRM-shape detail is practical but is the one element that could be trimmed or moved to a schema note, so it isn't a perfect 5.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description does the work of describing what comes back (paid status plus a permanent download link) and the shape variants. It is nearly complete for a one-parameter read tool, lacking only a statement of the unpaid/failure response.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There is only one parameter and schema coverage is 100%, so the schema already documents sessionId's type, pattern and origin. The description reinforces the cs_… format and the id's source, but adds no syntax or format detail beyond what the structured field provides, matching the baseline for high coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource: given a Stripe Checkout session id, report whether it is paid and return the permanent download link. It also scopes the supported order types (whole file, filtered rows, combined datasets), which clearly separates it from create_checkout and the dataset-query siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description ties the tool to its prerequisite by naming the source of the id ('from create_checkout' in the schema, 'cs_…' format in the text), so an agent knows this is the post-checkout status check. It stops short of explicitly saying when not to use it or naming a polling cadence, so it's clear context without exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Free, counts only. How two sets of places sit together: the share of set a within radius_miles of set b and of b within radius_miles of a, how many places overlap, and the county / zip / state / metro areas that have both, only a, or only b (top 10 of each named). Each set is a dataset, datasets or category plus filters, the same as count_locations; any US brand or kind of place works, including Overture lists from search_datasets. Example: {"a": {"dataset": ""}, "b": {"dataset": ""}, "radius_miles": 1, "by": "county"}.
| Name | Required | Description | Default |
|---|---|---|---|
| a | Yes | First set: dataset, datasets or category, plus filters | |
| b | Yes | Second set, given the same way | |
| by | No | Area unit for both/only-a/only-b (default county) | |
| in_state | No | Only areas in this state | |
| radius_miles | No | Distance that counts as together (default 1 mile) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the safety profile (readOnly, idempotent, non-destructive, openWorld), so the bar is low. The description still adds real behavioural context not in the annotations: it is 'Free, counts only', and the area breakdowns are truncated to the 'top 10 of each named' — a result-shaping limit an agent needs to know. It does not mention key requirements or error behaviour.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Front-loaded with the critical constraint ('Free, counts only'), followed by one dense but informative sentence on outputs, one sentence routing set specification to count_locations, and one compact example. The output enumeration is long but every clause maps to a distinct returned figure, so it earns its space.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a no-output-schema tool built on a highly nested schema, the description does the heavy lifting by describing the return content (shares, overlap count, top-10 area lists) and the pricing model. The safety profile is carried by annotations. Only edge details such as pricing beyond the top-10 listings or combination rules between radius_miles and the per-set near blocks are absent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents every parameter, including the deeply nested a/b set objects and the by/radius_miles/in_state top-level fields. The description adds only that sets are specified 'the same as count_locations' and an illustrative example that mirrors the schema shape, so the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a concrete analytical resource and enumerates exactly what is computed: share of set a within radius_miles of set b (and vice versa), count of overlapping places, and the county/zip/state/metro areas that hold both, only a, or only b. That is far more specific than a restated title. It stops short of a 5 because it does not explicitly distinguish itself from near-neighbours like relate_locations or query_locations, only from count_locations on the mechanics of set specification.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied by the purpose ('how two sets of places sit together') and it points to count_locations for how sets are built, but there is no explicit when-to-use statement, no exclusion of alternatives such as count_by_area or relate_locations, and no stated prerequisites. An agent must infer the triggering scenario from the output list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Free, counts only. Counts places per county / zip / state / metro for 1 to 4 labeled sets (each a dataset, datasets or category plus filters), and compares them: has (areas with at least one of every listed set) and lacks (areas with none of any listed set). E.g. counties that have set a but no set b; ZIPs where a closure-filtered set exists and another set still has places. Rows that cannot be placed are counted, never read as zero. Every area row can carry Census / NOAA figures (area_columns) and the areas can be ranked by one of them or by a count (order_by), so markets rank by demand; limit + offset page through every matching area (page.total, page.nextOffset), or all: true lists every matching area in one answer; top_values shows what kinds of place make up each count. WARNINGS lead the answer when they change how it reads: a set that could not be counted is never a zero (no has/lacks answer uses it), a lacks set too thin to support absence (few places, or an open-data brand list below the brand's own count), and areas in states a set's lists do not cover (those rows carry notCovered: there 0 means not covered, not none there). notes explain placement (duplicates counted once, state column vs county). REACH: a set's near (the same shape count_locations takes) keeps only its rows within radius_miles or drive_minutes of one point or of ANY of several points, BEFORE they are rolled up by area — so a regional operator is counted where it reaches, not nationally: "metros within 60 minutes by road of these three offices" is by=metro with near={points:[…], drive_minutes:60}. Drive time on this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. Radius is free everywhere; on /find, drive_minutes needs an issued key and the answer offers the radius link instead. Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free). FROM A CELL TO ITS ROWS: every area listed carries rows per set: the count, the price of those rows, a link, and args — the count_locations / query_locations / create_query_checkout call (the set's filters plus areas_in for that area) that returns exactly them, so a count cell is one call from its named rows. with_sample: true adds, on the first areas listed, the free preview behind each cell — the list's fixed published sample rows that fall inside the area, marked matches_your_question (never a sample of the area's places). Example: {"by": "county", "sets": [{"label": "a", "dataset": ""}, {"label": "b", "dataset": ""}], "has": ["a"], "lacks": ["b"], "in_state": "VA"}. Ranked by demand: {"by": "county", "sets": [{"label": "a", "dataset": ""}], "area_columns": ["county:population,median_household_income"], "order_by": {"field": "population"}, "limit": 100, "offset": 100}. Regional: {"by": "metro", "sets": [{"label": "ours", "dataset": "", "near": {"points": [{"place": "Richmond, VA"}, {"place": "Norfolk, VA"}], "radius_miles": 100}}]}.
| Name | Required | Description | Default |
|---|---|---|---|
| by | Yes | ||
| all | No | List every matching area, however many | |
| has | No | ||
| per | No | Counts per this many residents of each area from its Census population (100000 adds per_100k to every area and ranks by it), or per this many of per_field. | |
| sets | Yes | ||
| lacks | No | ||
| limit | No | Areas listed per call (default: every matching area when 500 or fewer match, else the first 100; at most 1000) | |
| offset | No | Areas to skip before the first listed: page through every matching area with limit + offset. The answer's page block gives total and the next offset (null on the last page). | |
| in_state | No | Only areas in this state | |
| order_by | No | Rank the areas: {"field": "median_household_income"} for the richest counties first, {"field": "a", "direction": "asc"} for the fewest of set a first. Unknown figures sort last. | |
| per_field | No | With per: the Census count the rate is over instead of population, e.g. per=1000 with per_field=construction_establishments adds per_1000_construction_establishments. Any count attribute: population, households, housing_units, establishments, employees, agriculture_establishments, mining_establishments, utilities_establishments, every <sector>_establishments / <sector>_employees, and every naics_<code>_establishments / naics_<code>_employees (2- to 6-digit NAICS, e.g. naics_8111_establishments). | |
| area_where | No | Only areas meeting a Census condition, each as "<kind>:<attribute><op><value>" about the same kind as by (or a state), e.g. "county:population>500000" with lacks for "counties over 500k people with no X". Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 191 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …. | |
| top_values | No | The most common values of one class column per area, per set (top on each area row): what kinds of place make up a count — {"column": "category"} shows that Elkhart County's 47 trailer dealers are 30 dealers, 12 manufacturers, 5 horse-trailer specialists. Free, bounded, and the same figures a filtered count gives. A name, address or contact column is refused: a free answer names classes, never records — the rows are in the file you buy. | |
| with_sample | No | Free. On the first 10 areas listed, each set's fixed published sample rows that fall inside the area (source_dataset, the list's columns, matches_your_question). The same published rows every question sees, so cells never add up to the file. | |
| area_columns | No | Census and NOAA figures RETURNED on every area row (in attributes), each as "<kind>:<attribute>,<attribute>" about the same kind as by (or "state:…" for the area's state, returned as state_<attribute>), e.g. ["county:population,median_household_income,households"]. Free. The vocabulary is the area manifest — the same words area_where takes (population, households, median household income, median age, growth since 2020, home values, <sector>_establishments / <sector>_employees by NAICS sector, and the NOAA weather elements); https://locationlists.com/find?areas=county&dataset=<slug>&area_columns=county:<attribute> refuses an unknown attribute by name and lists the nearest ones. Rank the areas by one with order_by. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only/idempotent/non-destructive, yet the description adds substantial beyond-schema behavior: WARNINGS lead the answer, uncounted sets are never zero, notCovered rows mean 'not covered' rather than 'none', placement notes (duplicates, state vs county), and the drive-time band limit and free/keyed distinction. That is exactly the extra context the lower bar with annotations rewards.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
It opens with the key constraint ('Free, counts only') and is organized into scannable blocks (REACH, FROM A CELL TO ITS ROWS, example payloads). It is very long and repeats drive-time caveats already present in the schema, which costs it a point, but the density is largely load-bearing for a 15-parameter tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
There is no output schema, so the description carries the full return-value burden and does so: rows/args per area, page.total and page.nextOffset, warnings, notes, and the free preview behind with_sample. For a tool of this complexity, nothing an agent needs to call or interpret a response is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 15 parameters and 73% schema coverage, the description still adds real meaning: what `near` does before roll-up, how `all`/`limit`/`offset`/page.total interact, what `per`/`per_field` compute, and what `top_values` and `with_sample` refuse to expose. It edges above the baseline-3 that high coverage would justify.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The opening states a specific verb and resource ('Counts places per county / zip / state / metro for 1 to 4 labeled sets') and adds the has/lacks comparison semantics, which cleanly separates it from count_locations (individual rows) without the agent needing to open either schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives strong conditions for use (has/lacks, ranking by area_columns or counts, per-resident rates, near-based regional counting) and explicitly routes work to siblings ('the count_locations / query_locations / create_query_checkout call ... that returns exactly them'). It never states when NOT to use this tool versus a plain count, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Free. How many rows of one dataset match a filter — on geography AND any other column (e.g. nonprofits with revenue_amt gt 2000000, dealers with dealerClass eq 'Elite'). Also reports how many rows were excluded only because a tested column was blank, so a thin column is not mistaken for a small answer; a small or empty answer says how many rows each condition removed and what the column really holds. Returns the exact card price of the matching rows, a link where the user can see and buy them, and the same rows in a cheaper list when one has them. Works for geography: city, state, county, zip, metro (a CBSA code or name: "all hospitals in the Philadelphia metro" is one call), areas_in (the county / ZIP / metro / state ids count_by_area returns), or near a place ("Los Angeles, CA"), zip or lat/lng within radius_miles or drive_minutes, on lists with coordinates. AREA DATA, free: area_where keeps rows whose county / ZIP / metro / state meets a Census condition (county:population>1000000), and area_columns adds those figures to every row as columns (county:population,median_household_income). COVERAGE: a list's coverage is measured on its rows (get_dataset coverageDetail: states and rows per state); a state the list has no rows in is said as not covered, never as none there. get_sample takes the same filters and returns the count plus the list's fixed free sample rows, marked matches_your_question. The result's next says exactly how to get every matching row. To cover several chains near one place, pass datasets or category (e.g. "retail" or "restaurant") and a total instead of dataset: one answer with counts per dataset, duplicates removed and up to 3 preview rows, one price and one file. Use get_dataset first for the column names. Scans the live file, so it can take several seconds on large datasets. Every number in the result is named: matched (rows meeting every filter), rowsRead (how many rows of the list were read to answer: the whole file for one of our lists, or, for an Overture list, only the index shards the question's area or place touches — so it can be far smaller than the list), maxRowsPerCall (the most rows one paid call returns), excludedBlank (rows dropped only because a tested column was blank), and price (soldBy says whether the list is sold as a file, by the row, or both; perRowUsd and perCallFeeUsd are the by-the-row terms, wallet the USDC total, breakdown each row source at its rate). To price exactly what one query_locations call returns — "the 10 nearest" — pass the same limit (and offset / order_by): slice then has that call's exact USDC amount line by line and the card price of the same rows. A paid call is billed for the rows it returns: each row at its own list's per-row rate (an Overture open-data row at $0.005; a row of a chain LocationLists sells its own list for, at that list's rate), plus a $0.01 per-call fee, rounded once to the nearest cent, at least $0.02, never more than the whole list. count_locations with the same filters, limit and offset quotes exactly that page, row source by row source (price.breakdown), before anything is paid.
| Name | Required | Description | Default |
|---|---|---|---|
| zip | No | Shortcut for where zip eq <value> | |
| city | No | Shortcut for where city eq <value> | |
| near | No | Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, lat+lng, or `points` (several of those: within reach of ANY of them, distance to the nearest). Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes; 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted. Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free). | |
| limit | No | Price a slice instead of every match: the first `limit` rows past `offset` in the delivery order — e.g. near a place with limit 10 for the 10 nearest — exactly the rows a purchase with the same limit returns. The answer's `slice` has their exact price, each row source at its own rate (open-data rows vs rows of lists we sell), and the card price. | |
| metro | No | Only rows in one metro area (CBSA): its 5-digit code ("37980") or its name ("Philadelphia", "Philadelphia, PA"). The same as areas_in {"by": "metro", "ids": [code]}: rows are placed the way count_by_area places them, so "all hospitals in the Philadelphia metro" is one count, one price and one purchase. | |
| state | No | Shortcut for where state eq <value>. Two-letter code. | |
| total | No | With datasets or category: rows wanted across all of them, split in proportion to each dataset's matches | |
| where | No | Conditions on any column, all of which must hold. Blank cells never satisfy a comparison; the response counts rows excluded only because a tested column was blank. A small or empty answer says how many rows each condition removed and what the column really holds. | |
| county | No | Shortcut for where county eq <value> | |
| offset | No | With limit: skip this many matching rows, as a paged purchase does, so the quote is for that page | |
| dataset | No | Dataset slug, e.g. nonprofits-va. To combine several, give datasets or category instead. | |
| exclude | No | With category or datasets: dataset slugs left out (a category minus one of its members, e.g. a list's competitors are its own category with itself excluded) | |
| permits | No | Only rows with a BUILDING PERMIT nearby, read live from the city's own permit register at question time — e.g. {"dataset": "<slug>", "city": "Austin", "state": "TX", "permits": {"days": 90, "type": "commercial", "min_usd": 1000000}} for places near a commercial permit issued in the last 90 days valued at $1M or more. days: issued in the last N days (default 365, and the answer says the window was defaulted); type: one of any, new_construction, commercial, residential, renovation, demolition, electrical, plumbing, mechanical, roofing, solar, sign, pool; min_usd: the valuation floor; metres: how close a permit must be (default 60). One city register per question, chosen from the base set's city, a ZIP in it, its county, or a state with one register; the cities read now: New York, NY; Los Angeles, CA; Chicago, IL; Dallas, TX; Austin, TX; San Francisco, CA; Seattle, WA; Nashville, TN. A kind or amount a city's register cannot tell is refused in words, never answered as any. The answer is free (counts, a preview, the price of the matching rows of our list) and its openData block names the register, its publisher, licence and when it was read. The same question on /find: …&permits=1&permits_days=90&permits_type=commercial&permits_min_usd=1000000. Not sold through a checkout yet: the answer's link is the page. | |
| areas_in | No | Only rows in these areas, e.g. {"by": "county", "ids": ["18039"]} for Elkhart County, IN — the ids count_by_area returns. Rows are placed by county_fips, county + state, coordinates or ZIP, the same as count_by_area. | |
| category | No | Instead of dataset: every dataset of one kind — "retail" (store chains), an industry or subcategory, or a kind of business such as "restaurant" or "bank branch" (search_datasets names these) | |
| datasets | No | Instead of dataset: several dataset slugs answered as one (one count, one preview, one price, one file) | |
| order_by | No | With limit: the order the slice is taken in, as a purchase takes it. Without it, nearest first with `near`, else file order. | |
| area_where | No | Only rows whose county/zip/state/metro meets a Census condition, each as "<kind>:<attribute><op><value>" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. "county:population>1000000". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 191 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …. | |
| area_columns | No | Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as "<kind>:<attribute>,<attribute>", e.g. "county:population,median_household_income" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy, after the area's id and name (county_fips, county_name; zip_code; metro_cbsa, metro_name; state_code, state_name). Free. The same attribute words area_where takes (listed there). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations only cover read-only/idempotent/non-destructive, and the description adds rich operational context well beyond that: the scan hits the live file and 'can take several seconds', blank-only exclusions are counted separately, drive-time requires an external routing service with a documented fallback to radius_miles, and the full billing model (per-row rates, $0.01 call fee, rounding floor/ceiling) is spelled out with the exact keys returned.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The first sentence is well front-loaded, but the body is roughly 800 words of dense run-on text, and the drive-time caveat ('Drive time depends on an outside routing service…') is reproduced almost verbatim twice, plus a third time in the nested schema. Significant material could be cut without loss.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 19-parameter, nested, no-output-schema tool, the description is unusually complete: it names the result fields (matched, rowsRead, maxRowsPerCall, excludedBlank, price with soldBy/perRowUsd/perCallFeeUsd/wallet/breakdown) and the price-relevant interactions. Minor gaps remain around auth on paid paths and any rate limits, but nothing essential is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already explains all 19 parameters in detail; per the rubric the baseline is 3. The description does add cross-parameter behavior (limit/offset/order_by cooperating to quote exactly one page), but most of what it says about parameters restates the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The opening states a specific verb and resource with scope: 'How many rows of one dataset match a filter — on geography AND any other column.' It also names the related siblings it is not (get_sample returns the count plus sample rows; count_by_area returns area ids; query_locations prices a page), so an agent can place it in the family without opening schemas.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is well routed: 'Use get_dataset first for the column names', use datasets/category + total 'instead of dataset' for multi-chain questions, and area_where/area_columns for free Census context. It never states a clean when-not-to-use rule (e.g. vs. query_locations), so it stops short of 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Opens a Stripe Checkout session for one dataset and returns the payment URL plus the session id. Give the URL to the user to pay (card, Apple Pay, Google Pay). After payment Stripe emails them a permanent download link; use check_order with the session id to confirm and fetch it. Does not charge anything by itself.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Dataset slug | |
| No | Buyer email, if known — prefills Checkout and is where the download link is sent |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=false, idempotentHint=false, destructiveHint=false, openWorldHint=true, but the description adds the crucial nuance that the tool 'does not charge anything by itself' and that fulfillment happens asynchronously by email after payment. That side-effect scoping is exactly the context annotations cannot convey.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three short sentences, no filler, with the core action and return value front-loaded before the payment workflow and follow-up tool. Every sentence carries information the agent needs.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description supplies the return values itself (payment URL and session id), explains the post-payment flow, and names the follow-up tool. Nothing needed to invoke or sequence it correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so both slug and email are already documented. The description reinforces the email's downstream role ('where the download link is sent') and the single-dataset constraint, but adds nothing new about formats or edge cases, so the baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource ('Opens a Stripe Checkout session for one dataset') and names exactly what it returns (payment URL plus session id), so an agent can distinguish it from get_dataset or buy_dataset at a glance.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives the full operating context: hand the URL to the user, then use check_order with the session id to confirm and fetch the permanent download link. The only gap is that it never contrasts this with the sibling buy_dataset, so the choice between the two is left to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
For buyers paying by card (no wallet needed): opens a Stripe Checkout for just the rows of one dataset that match a filter, and returns the payment URL to give the user. Takes the same filters as count_locations (state/city/county/zip, where on any column, near, order_by) and up to 10,000 rows. It counts the matches first, so the buyer pays only for rows that exist: the data price is the same per-row price query_locations charges, plus a card processing fee (2.9% + $0.30) added on top, one price the buyer sees at checkout. After payment the buyer is emailed a CSV download link; check_order with the session id returns it too. No match, a bad column, a distance search on a list without coordinates, or a subset that would cost more than the whole file returns an explanation and creates no checkout — nothing is charged. Agents with a USDC wallet should call query_locations instead. To cover several chains near one place, pass datasets or category and a total instead of dataset: one answer, one price and one checkout for one CSV (source_dataset names each row's dataset, duplicates removed). shape: hubspot | salesforce delivers the CSV with that CRM's import column names first (the download link also takes ?shape= later).
| Name | Required | Description | Default |
|---|---|---|---|
| zip | No | Shortcut for where zip eq <value> | |
| city | No | Shortcut for where city eq <value> | |
| near | No | Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, lat+lng, or `points` (several of those: within reach of ANY of them, distance to the nearest). Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes; 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted. Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free). | |
| No | Buyer email, if known — prefills Checkout and is where the download link is sent | ||
| limit | No | Most rows to buy (max 10,000). Default: every match up to the max. Priced on the rows that actually match, never more. | |
| metro | No | Only rows in one metro area (CBSA): its 5-digit code ("37980") or its name ("Philadelphia", "Philadelphia, PA"). The same as areas_in {"by": "metro", "ids": [code]}: rows are placed the way count_by_area places them, so "all hospitals in the Philadelphia metro" is one count, one price and one purchase. | |
| shape | No | CRM-ready columns: hubspot (Company name, Company domain name, Website URL, Phone number, Street address, Street address 2, City, State/Region, Postal code, Country/Region, Industry, Description) or salesforce (Name, Website, Phone, BillingStreet, BillingCity, BillingState, BillingPostalCode, BillingCountry, Industry, Description) first, then every other column of the list under its own name. Nothing is dropped; a CRM column the list lacks is present and blank. | |
| state | No | Shortcut for where state eq <value>. Two-letter code. | |
| total | No | With datasets or category: rows wanted across all of them, split in proportion to each dataset's matches | |
| where | No | Conditions on any column, all of which must hold. Blank cells never satisfy a comparison; the response counts rows excluded only because a tested column was blank. A small or empty answer says how many rows each condition removed and what the column really holds. | |
| county | No | Shortcut for where county eq <value> | |
| relate | No | ||
| dataset | No | Dataset slug, e.g. nonprofits-va. To combine several, give datasets or category instead. | |
| exclude | No | With category or datasets: dataset slugs left out (a category minus one of its members, e.g. a list's competitors are its own category with itself excluded) | |
| permits | No | Only rows with a BUILDING PERMIT nearby, read live from the city's own permit register at question time — e.g. {"dataset": "<slug>", "city": "Austin", "state": "TX", "permits": {"days": 90, "type": "commercial", "min_usd": 1000000}} for places near a commercial permit issued in the last 90 days valued at $1M or more. days: issued in the last N days (default 365, and the answer says the window was defaulted); type: one of any, new_construction, commercial, residential, renovation, demolition, electrical, plumbing, mechanical, roofing, solar, sign, pool; min_usd: the valuation floor; metres: how close a permit must be (default 60). One city register per question, chosen from the base set's city, a ZIP in it, its county, or a state with one register; the cities read now: New York, NY; Los Angeles, CA; Chicago, IL; Dallas, TX; Austin, TX; San Francisco, CA; Seattle, WA; Nashville, TN. A kind or amount a city's register cannot tell is refused in words, never answered as any. The answer is free (counts, a preview, the price of the matching rows of our list) and its openData block names the register, its publisher, licence and when it was read. The same question on /find: …&permits=1&permits_days=90&permits_type=commercial&permits_min_usd=1000000. Not sold through a checkout yet: the answer's link is the page. | |
| areas_in | No | Only rows in these areas, e.g. {"by": "county", "ids": ["18039"]} for Elkhart County, IN — the ids count_by_area returns. Rows are placed by county_fips, county + state, coordinates or ZIP, the same as count_by_area. | |
| category | No | Instead of dataset: every dataset of one kind — "retail" (store chains), an industry or subcategory, or a kind of business such as "restaurant" or "bank branch" (search_datasets names these) | |
| datasets | No | Instead of dataset: several dataset slugs answered as one (one count, one preview, one price, one file) | |
| order_by | No | Return the top rows by one column, e.g. {field: 'revenue_amt'} for the largest first. Blanks sort last. | |
| area_where | No | Only rows whose county/zip/state/metro meets a Census condition, each as "<kind>:<attribute><op><value>" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. "county:population>1000000". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 191 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …. | |
| area_columns | No | Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as "<kind>:<attribute>,<attribute>", e.g. "county:population,median_household_income" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy, after the area's id and name (county_fips, county_name; zip_code; metro_cbsa, metro_name; state_code, state_name). Free. The same attribute words area_where takes (listed there). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Goes well beyond the annotations: counts matches before charging, discloses the card fee (2.9% + $0.30) and that it is one price shown at checkout, explains post-payment email delivery and that check_order can retrieve the link, and states nothing is charged on no-match/bad-column/oversized-subset. This is exactly the mutation/charging context annotations cannot carry.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Front-loaded with purpose, pricing, and the wallet alternative; every sentence carries information. It is a single dense paragraph that mixes pricing, errors, multi-dataset, and shape, so it is long but not padded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 21-param, no-output-schema, charging tool, the description covers the non-schema essentials (cost, failure semantics, cross-tool routing, multi-dataset pricing, shape). Return values are not documented beyond the payment URL and CSV link, but the sibling check_order covers retrieval.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 95%, so the baseline is 3, and the description adds real meaning on top: the 10,000-row cap, how `limit`/`total` relate to pricing, how `datasets`/`category` collapse to one price and one CSV, and how `shape` reorders CRM columns. It does not attempt to enumerate the 21 params, which the schema already does.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Specific verb+resource: 'opens a Stripe Checkout for just the rows of one dataset that match a filter, and returns the payment URL'. It also distinguishes itself from siblings by naming count_locations (same filters) and query_locations (the wallet path).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit routing: 'Agents with a USDC wallet should call query_locations instead', plus the multi-chain case ('pass datasets or category and a total instead of dataset'). Failure conditions are spelled out so the agent knows when no checkout is created.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Free. Emails the user a plain-English quote for exactly this request: how many rows match, the card price, a few of the matches and a card checkout link, so they can pay later, from any device, or forward it to whoever holds the card. Takes the same arguments as count_locations: dataset, or datasets / category with total, plus filters. BEFORE calling: ask the user for their email and whether to send it. Pass an email only if the user gave it to you in this conversation; never guess or invent one. Nothing is charged and nothing is bought; the price is checked again when they open the link.
| Name | Required | Description | Default |
|---|---|---|---|
| zip | No | Shortcut for where zip eq <value> | |
| city | No | Shortcut for where city eq <value> | |
| near | No | Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, lat+lng, or `points` (several of those: within reach of ANY of them, distance to the nearest). Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes; 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted. Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free). | |
| Yes | The user's email, as they gave it | ||
| metro | No | Only rows in one metro area (CBSA): its 5-digit code ("37980") or its name ("Philadelphia", "Philadelphia, PA"). The same as areas_in {"by": "metro", "ids": [code]}: rows are placed the way count_by_area places them, so "all hospitals in the Philadelphia metro" is one count, one price and one purchase. | |
| state | No | Shortcut for where state eq <value>. Two-letter code. | |
| total | No | With datasets or category: rows wanted across all of them, split in proportion to each dataset's matches | |
| where | No | Conditions on any column, all of which must hold. Blank cells never satisfy a comparison; the response counts rows excluded only because a tested column was blank. A small or empty answer says how many rows each condition removed and what the column really holds. | |
| county | No | Shortcut for where county eq <value> | |
| dataset | No | Dataset slug, e.g. nonprofits-va. To combine several, give datasets or category instead. | |
| exclude | No | With category or datasets: dataset slugs left out (a category minus one of its members, e.g. a list's competitors are its own category with itself excluded) | |
| permits | No | Only rows with a BUILDING PERMIT nearby, read live from the city's own permit register at question time — e.g. {"dataset": "<slug>", "city": "Austin", "state": "TX", "permits": {"days": 90, "type": "commercial", "min_usd": 1000000}} for places near a commercial permit issued in the last 90 days valued at $1M or more. days: issued in the last N days (default 365, and the answer says the window was defaulted); type: one of any, new_construction, commercial, residential, renovation, demolition, electrical, plumbing, mechanical, roofing, solar, sign, pool; min_usd: the valuation floor; metres: how close a permit must be (default 60). One city register per question, chosen from the base set's city, a ZIP in it, its county, or a state with one register; the cities read now: New York, NY; Los Angeles, CA; Chicago, IL; Dallas, TX; Austin, TX; San Francisco, CA; Seattle, WA; Nashville, TN. A kind or amount a city's register cannot tell is refused in words, never answered as any. The answer is free (counts, a preview, the price of the matching rows of our list) and its openData block names the register, its publisher, licence and when it was read. The same question on /find: …&permits=1&permits_days=90&permits_type=commercial&permits_min_usd=1000000. Not sold through a checkout yet: the answer's link is the page. | |
| areas_in | No | Only rows in these areas, e.g. {"by": "county", "ids": ["18039"]} for Elkhart County, IN — the ids count_by_area returns. Rows are placed by county_fips, county + state, coordinates or ZIP, the same as count_by_area. | |
| category | No | Instead of dataset: every dataset of one kind — "retail" (store chains), an industry or subcategory, or a kind of business such as "restaurant" or "bank branch" (search_datasets names these) | |
| datasets | No | Instead of dataset: several dataset slugs answered as one (one count, one preview, one price, one file) | |
| area_where | No | Only rows whose county/zip/state/metro meets a Census condition, each as "<kind>:<attribute><op><value>" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. "county:population>1000000". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 191 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …. | |
| area_columns | No | Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as "<kind>:<attribute>,<attribute>", e.g. "county:population,median_household_income" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy, after the area's id and name (county_fips, county_name; zip_code; metro_cbsa, metro_name; state_code, state_name). Free. The same attribute words area_where takes (listed there). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already say not read-only, not destructive, open-world, non-idempotent; the description adds real behavior beyond them by clarifying that 'nothing is charged and nothing is bought' and that 'the price is checked again when they open the link' — an important caveat about the delivered artifact being stale. It does not cover send duplication/rate limits or whether a second email can be sent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Front-loaded with the cost ('Free.') then the effect, then the call prerequisites, in three dense sentences with no filler. The arguments sentence is slightly crammed, but nothing is wasted.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
There is no output schema, and the description compensates by describing exactly what the email contains and what the checkout link does. Combined with the stated email-permission requirement and the 'same arguments as count_locations' bridge, an agent has everything needed to call this correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 17 parameters at full schema coverage, the schema carries the per-parameter burden and baseline is 3. The description earns above baseline by telling the agent the arguments mirror count_locations ('dataset, or datasets / category with total, plus filters'), which lets a prior count_locations call be replayed directly into this tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb (emails) and resource (a plain-English quote for this exact request) and enumerates the payload: matching-row count, card price, sample matches, and a card checkout link. That content listing distinguishes it from get_quote and create_checkout without opening either schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Gives an explicit precondition ('BEFORE calling: ask the user for their email and whether to send it') plus a hard boundary: never guess or invent an address, only use one given in this conversation. It never routes the agent to the obvious alternative (get_quote, which returns a quote in-conversation), so 4 rather than 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Full record for one dataset: fields with descriptions, record and state counts, coverage measured on the rows (a list in 14 states says "partial U.S.: 14 states"; coverageDetail lists the states, rows per state and the states with none), whether it can be searched by distance, advertised refresh cadence AND the real last-modified date of the file, FAQs, sample URL and the dataset's page on locationlists.com.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | Dataset slug from search_datasets, e.g. bobcat-dealers |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint/idempotentHint/destructiveHint=false, so safety is covered. The description goes beyond that by explaining interpretive behavior an agent could not otherwise know: coverage is measured on rows ("partial U.S.: 14 states"), coverageDetail breaks down rows per state, and advertised refresh cadence may differ from the real last-modified date. That semantic context is genuinely additive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single dense sentence, but it is front-loaded with the core purpose ("Full record for one dataset") before the enumeration of fields. The parenthetical coverage example earns its place by clarifying a non-obvious metric; nothing is redundant.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
There is no output schema, so the description carries the return-value burden and does so thoroughly, naming fields, counts, coverage detail, distance-searchability, refresh cadence, FAQs and URLs. What is missing is the usage framing (why this vs. searching or buying), the one gap that keeps it from being fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Only one parameter, and schema description coverage is 100%, so the baseline is 4. The description adds nothing about slug handling, but with a single fully documented parameter there is nothing meaningful left to compensate for.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb+resource ("Full record for one dataset") and then enumerates the exact contents of that record, which is far more specific than a generic "get" description. It does not name or contrast with siblings like search_datasets or buy_dataset, so an agent must infer the relationship.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No when-to-use or when-not-to-use guidance appears in the description. The only routing hint ("slug from search_datasets") lives in the schema's parameter description, not the tool description, so the agent gets no explicit statement that this is the detail-lookup follow-up to a search.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Line-item prices and total for a list of dataset slugs, each with how it is sold (soldBy: file, row, or both) — the same rule create_checkout, buy_dataset, create_query_checkout and query_locations enforce, so a quote never offers a route checkout refuses. A list sold by the row only (an Overture Maps list) is quoted at its per-row rate with the route named, not as a file; count_locations with filters gives the exact price of the rows. Every list sold by the row is quoted with its perRow rate and the per-call fee, next to any file price. A paid call is billed for the rows it returns: each row at its own list's per-row rate (an Overture open-data row at $0.005; a row of a chain LocationLists sells its own list for, at that list's rate), plus a $0.01 per-call fee, rounded once to the nearest cent, at least $0.02, never more than the whole list. count_locations with the same filters, limit and offset quotes exactly that page, row source by row source (price.breakdown), before anything is paid. How a list is sold: our own lists are sold as a whole file (create_checkout / buy_dataset), and those of 5,000 records or more are also sold by the row (query_locations / create_query_checkout); smaller lists are sold whole only. Open map data lists (soldBy "row" in get_dataset) are sold by the row ONLY, at any size — there is no file to buy. One card checkout downloads up to 10,000 rows at once; up to 150,000 rows, an answer's buy link still sells them all, and the file is built after payment and its download link emailed. For more, buy the whole file where the list has one, narrow the question to a state, metro or area, or email kyle@locationlists.com for the whole file. If a bundle covers several requested brands for less, it says so.
| Name | Required | Description | Default |
|---|---|---|---|
| slugs | Yes | Dataset slugs |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so safety is covered. The description adds valuable behavioral detail: pricing rules (per-row, per-call fee, rounding, min $0.02, max list price), how soldBy affects quoting, and checkout limits. However, it does not describe the return structure (line items, total) in a structured way, and some pricing details are verbose but not fully clarifying for an agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is overly long and rambling, with multiple sentences repeating pricing rules and checkout limits. It is front-loaded with the core purpose but then diverges into extensive edge cases, making it hard to scan. Several sentences could be consolidated or moved to a separate pricing guide.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's read-only nature, no output schema, and one required parameter, the description is complete enough to call it, but it omits key contextual details: the return shape (line items, breakdown structure), whether the response includes a total, and how errors are handled for invalid slugs. It focuses heavily on pricing rules rather than the tool's output, leaving gaps for an agent to interpret results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the single 'slugs' parameter is fully documented in the schema. The description adds context that slugs represent dataset lists and that soldBy matters, but does not provide additional semantics beyond what the schema already provides for the parameter itself.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource: 'Line-item prices and total for a list of dataset slugs,' and immediately distinguishes itself by linking to the pricing rule enforced by create_checkout, buy_dataset, and others. It is clear what the tool does, though it does not explicitly name 'get_quote' vs a sibling like count_locations as the alternative.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives clear usage context: it is for quoting prices before checkout, and it names how to get exact row prices via count_locations with filters. It implies when to use this tool (to preview costs) but does not explicitly state when not to use it or name a direct alternative for simple file-only quotes.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Free. Real rows from the live file, as JSON plus CSV text. Show these to the user so they can judge the fields and quality. Without filters: up to 10 rows spread across the whole dataset. With filters (the same ones count_locations takes: city, state, county, zip, metro, where on any column, areas_in (county / ZIP / metro / state ids from count_by_area), area_where, area_columns (Census figures as columns on every row), or near a place such as {place: "Los Angeles, CA", radius_miles: 25} or {place: "Richmond, VA", drive_minutes: 30} on lists with coordinates): how many rows match, plus the list's FIXED free sample rows — the same published rows whatever the filter, each marked matches_your_question, the ones this filter matched FIRST (default 3 rows; rows up to the published sample's size shows them all, and samplePublished / sampleMatched say how many that is) — so the user can see real stores and the real column shape before deciding, with the exact count and price of the matches. The rows shown are fixed per list so that free answers cannot be composed into the file; the count, the coverage, the columns and the price are exact for the filter. To see what KINDS of place make up a count in an area (dealers vs manufacturers, say), use count_by_area with top_values on a class column. The result's next says exactly how to get every matching row. To preview several chains at once, pass datasets or category (e.g. "retail" or "restaurant") instead of slug: one combined answer with counts per dataset, duplicates removed and up to 3 rows across them. shape: hubspot | salesforce returns the same rows with that CRM's import column names first (the exact header row the paid file will have with the same shape), every other column after.
| Name | Required | Description | Default |
|---|---|---|---|
| zip | No | Shortcut for where zip eq <value> | |
| city | No | Shortcut for where city eq <value> | |
| near | No | Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, lat+lng, or `points` (several of those: within reach of ANY of them, distance to the nearest). Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes; 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted. Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free). | |
| rows | No | Rows to return (default 10; with filters default 3, up to the published sample's size — samplePublished in the answer) | |
| slug | No | Dataset slug. Or datasets / category to preview several at once. | |
| metro | No | Only rows in one metro area (CBSA): its 5-digit code ("37980") or its name ("Philadelphia", "Philadelphia, PA"). The same as areas_in {"by": "metro", "ids": [code]}: rows are placed the way count_by_area places them, so "all hospitals in the Philadelphia metro" is one count, one price and one purchase. | |
| shape | No | CRM-ready columns: hubspot (Company name, Company domain name, Website URL, Phone number, Street address, Street address 2, City, State/Region, Postal code, Country/Region, Industry, Description) or salesforce (Name, Website, Phone, BillingStreet, BillingCity, BillingState, BillingPostalCode, BillingCountry, Industry, Description) first, then every other column of the list under its own name. Nothing is dropped; a CRM column the list lacks is present and blank. | |
| state | No | Shortcut for where state eq <value>. Two-letter code. | |
| total | No | With datasets or category: rows wanted across all of them, split in proportion to each dataset's matches | |
| where | No | Conditions on any column, all of which must hold. Blank cells never satisfy a comparison; the response counts rows excluded only because a tested column was blank. A small or empty answer says how many rows each condition removed and what the column really holds. | |
| county | No | Shortcut for where county eq <value> | |
| exclude | No | With category or datasets: dataset slugs left out (a category minus one of its members, e.g. a list's competitors are its own category with itself excluded) | |
| permits | No | Only rows with a BUILDING PERMIT nearby, read live from the city's own permit register at question time — e.g. {"dataset": "<slug>", "city": "Austin", "state": "TX", "permits": {"days": 90, "type": "commercial", "min_usd": 1000000}} for places near a commercial permit issued in the last 90 days valued at $1M or more. days: issued in the last N days (default 365, and the answer says the window was defaulted); type: one of any, new_construction, commercial, residential, renovation, demolition, electrical, plumbing, mechanical, roofing, solar, sign, pool; min_usd: the valuation floor; metres: how close a permit must be (default 60). One city register per question, chosen from the base set's city, a ZIP in it, its county, or a state with one register; the cities read now: New York, NY; Los Angeles, CA; Chicago, IL; Dallas, TX; Austin, TX; San Francisco, CA; Seattle, WA; Nashville, TN. A kind or amount a city's register cannot tell is refused in words, never answered as any. The answer is free (counts, a preview, the price of the matching rows of our list) and its openData block names the register, its publisher, licence and when it was read. The same question on /find: …&permits=1&permits_days=90&permits_type=commercial&permits_min_usd=1000000. Not sold through a checkout yet: the answer's link is the page. | |
| areas_in | No | Only rows in these areas, e.g. {"by": "county", "ids": ["18039"]} for Elkhart County, IN — the ids count_by_area returns. Rows are placed by county_fips, county + state, coordinates or ZIP, the same as count_by_area. | |
| category | No | Instead of dataset: every dataset of one kind — "retail" (store chains), an industry or subcategory, or a kind of business such as "restaurant" or "bank branch" (search_datasets names these) | |
| datasets | No | Instead of dataset: several dataset slugs answered as one (one count, one preview, one price, one file) | |
| area_where | No | Only rows whose county/zip/state/metro meets a Census condition, each as "<kind>:<attribute><op><value>" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. "county:population>1000000". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 191 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …. | |
| area_columns | No | Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as "<kind>:<attribute>,<attribute>", e.g. "county:population,median_household_income" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy, after the area's id and name (county_fips, county_name; zip_code; metro_cbsa, metro_name; state_code, state_name). Free. The same attribute words area_where takes (listed there). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, non-destructive, so the bar is lower, yet the description adds substantial behavior: the sample rows are FIXED per list so free answers cannot be composed into the file, samplePublished/sampleMatched report the real counts, drive-time search may be unavailable and should be retried with radius_miles, and permits are read live from city registers with refusal-in-words semantics. This is rich disclosure well beyond what the annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The essential idea ('free sample rows') is front-loaded, but the body is a single sprawling paragraph with deeply nested parentheticals (notably the near/filters sentence) that is very hard to parse. Much of the filter and shape detail duplicates the 100%-covered schema, so length is not earning its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For an 18-parameter, nested-schema tool with no output schema and no required parameters, the description is unusually complete: it names the response fields that matter (next, samplePublished, sampleMatched, distance_miles, within_drive_minutes, matches_your_question) and explains the free/paid boundary, so an agent can call it and interpret the result correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 18 parameters and baseline would be 3. The description still adds meaning the schema does not carry alone: it ties the filter set to count_locations ('the same ones count_locations takes'), states unconditional defaults (10 rows unfiltered vs 3 filtered), and explains the sample-size cap interaction with the published sample.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The opening states the verb and resource precisely — free real rows from the live file returned as JSON plus CSV — and frames the intent (show the user so they can judge fields and quality). It distinguishes itself from count_by_area in one place, but does not clearly separate itself from count_locations or query_locations, which share almost all the same filter parameters.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Gives clear context ('show these to the user so they can judge the fields and quality... before deciding') and routes to a sibling for a specific case: 'To see what KINDS of place make up a count in an area, use count_by_area with top_values on a class column.' It also explains the preview-several-chains mode. It never states when NOT to use this tool versus count_locations/query_locations, which is the main missing piece.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Return matching rows from one dataset, filtered on ANY of its columns — state/city/county/zip shortcuts plus where conditions with numeric comparisons (e.g. [{field:"revenue_amt",op:"gt",value:2000000}]), sorted with order_by and paged with offset. near ({place:"Topeka, KS"}, a zip, or lat+lng, optional radius_miles or drive_minutes) returns the closest rows first with distance_miles, on lists with coordinates — so "10 banks closest to Topeka" is one call for 10 rows. get_dataset lists the columns; count_locations (free) tells you how many rows match and what fetching them costs before you pay. Priced per row in USDC via x402 and settled only after the rows are produced, so a failed call costs nothing. The rate is derived from the dataset: roughly 2x its list price spread over its record count, so a small slice of a big file is cents. By default you get and pay for every matching row, up to 100 to 1,000 rows per call depending on how wide the dataset's rows are (count_locations reports maxRowsPerCall); pass limit for fewer. Call it without payment first: the result is an x402 PaymentRequired quote with the exact amount and quote (the same rows, each source at its rate, as count_locations' slice), and nothing is charged until you retry with payment. A paid call is billed for the rows it returns: each row at its own list's per-row rate (an Overture open-data row at $0.005; a row of a chain LocationLists sells its own list for, at that list's rate), plus a $0.01 per-call fee, rounded once to the nearest cent, at least $0.02, never more than the whole list. count_locations with the same filters, limit and offset quotes exactly that page, row source by row source (price.breakdown), before anything is paid. How a list is sold: our own lists are sold as a whole file (create_checkout / buy_dataset), and those of 5,000 records or more are also sold by the row (query_locations / create_query_checkout); smaller lists are sold whole only. Open map data lists (soldBy "row" in get_dataset) are sold by the row ONLY, at any size — there is no file to buy. One card checkout downloads up to 10,000 rows at once; up to 150,000 rows, an answer's buy link still sells them all, and the file is built after payment and its download link emailed. For more, buy the whole file where the list has one, narrow the question to a state, metro or area, or email kyle@locationlists.com for the whole file. To cover several chains near one place, pass datasets or category and a total (up to 1,000 rows) instead of dataset: one answer, one price and one file, with source_dataset naming each row's dataset and duplicates removed; inside a combined answer, small datasets are sold by the row too.
| Name | Required | Description | Default |
|---|---|---|---|
| zip | No | Shortcut for where zip eq <value> | |
| city | No | Shortcut for where city eq <value> | |
| near | No | Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, lat+lng, or `points` (several of those: within reach of ANY of them, distance to the nearest). Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes; 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted. Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free). | |
| limit | No | Rows to return and pay for. Default: every matching row, up to the most one call can return. That maximum depends on how wide the dataset's rows are, from 100 to 1,000; count_locations reports it as maxRowsPerCall, and a larger limit is reduced to it before pricing. | |
| metro | No | Only rows in one metro area (CBSA): its 5-digit code ("37980") or its name ("Philadelphia", "Philadelphia, PA"). The same as areas_in {"by": "metro", "ids": [code]}: rows are placed the way count_by_area places them, so "all hospitals in the Philadelphia metro" is one count, one price and one purchase. | |
| shape | No | CRM-ready columns: hubspot (Company name, Company domain name, Website URL, Phone number, Street address, Street address 2, City, State/Region, Postal code, Country/Region, Industry, Description) or salesforce (Name, Website, Phone, BillingStreet, BillingCity, BillingState, BillingPostalCode, BillingCountry, Industry, Description) first, then every other column of the list under its own name. Nothing is dropped; a CRM column the list lacks is present and blank. | |
| state | No | Shortcut for where state eq <value>. Two-letter code. | |
| total | No | With datasets or category: rows to return and pay for across all of them (default every distinct match, up to 1,000), in one payment | |
| where | No | Conditions on any column, all of which must hold. Blank cells never satisfy a comparison; the response counts rows excluded only because a tested column was blank. A small or empty answer says how many rows each condition removed and what the column really holds. | |
| county | No | Shortcut for where county eq <value> | |
| offset | No | Skip this many matching rows, to page past the first call | |
| relate | No | ||
| dataset | No | Dataset slug, e.g. nonprofits-va. To combine several, give datasets or category instead. | |
| exclude | No | With category or datasets: dataset slugs left out (a category minus one of its members, e.g. a list's competitors are its own category with itself excluded) | |
| permits | No | Only rows with a BUILDING PERMIT nearby, read live from the city's own permit register at question time — e.g. {"dataset": "<slug>", "city": "Austin", "state": "TX", "permits": {"days": 90, "type": "commercial", "min_usd": 1000000}} for places near a commercial permit issued in the last 90 days valued at $1M or more. days: issued in the last N days (default 365, and the answer says the window was defaulted); type: one of any, new_construction, commercial, residential, renovation, demolition, electrical, plumbing, mechanical, roofing, solar, sign, pool; min_usd: the valuation floor; metres: how close a permit must be (default 60). One city register per question, chosen from the base set's city, a ZIP in it, its county, or a state with one register; the cities read now: New York, NY; Los Angeles, CA; Chicago, IL; Dallas, TX; Austin, TX; San Francisco, CA; Seattle, WA; Nashville, TN. A kind or amount a city's register cannot tell is refused in words, never answered as any. The answer is free (counts, a preview, the price of the matching rows of our list) and its openData block names the register, its publisher, licence and when it was read. The same question on /find: …&permits=1&permits_days=90&permits_type=commercial&permits_min_usd=1000000. Not sold through a checkout yet: the answer's link is the page. | |
| areas_in | No | Only rows in these areas, e.g. {"by": "county", "ids": ["18039"]} for Elkhart County, IN — the ids count_by_area returns. Rows are placed by county_fips, county + state, coordinates or ZIP, the same as count_by_area. | |
| category | No | Instead of dataset: every dataset of one kind — "retail" (store chains), an industry or subcategory, or a kind of business such as "restaurant" or "bank branch" (search_datasets names these) | |
| datasets | No | Instead of dataset: several dataset slugs answered as one (one count, one preview, one price, one file) | |
| order_by | No | Return the top rows by one column, e.g. {field: 'revenue_amt'} for the largest first. Blanks sort last. | |
| area_where | No | Only rows whose county/zip/state/metro meets a Census condition, each as "<kind>:<attribute><op><value>" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. "county:population>1000000". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 191 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …. | |
| area_columns | No | Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as "<kind>:<attribute>,<attribute>", e.g. "county:population,median_household_income" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy, after the area's id and name (county_fips, county_name; zip_code; metro_cbsa, metro_name; state_code, state_name). Free. The same attribute words area_where takes (listed there). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations only declare read-only/open-world; the description adds the payment protocol (PaymentRequired quote first, nothing charged until retry with payment), the per-row billing formula with the $0.01 call fee and $0.02 floor, the 100–1,000 row per-call cap, and the drive-time dependency with a radius_miles fallback. This is far beyond what the annotations convey.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The opening sentence is well front-loaded, but the description then runs on for a full page of sales policy — whole-file vs by-the-row selling, 10,000-row card checkouts, 150,000-row links, an email address — much of which does not help an agent invoke this tool, and the drive-time fallback text is duplicated in the `near` schema.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, the description does supply return-shape context (distance_miles, source_dataset, price.breakdown, maxRowsPerCall) and covers the pricing/quoting flow an agent must understand before calling a paid tool. Given 21 parameters with 95% schema coverage, the remaining gaps are minor.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is already 95%, so the baseline is 3; the description still adds value by explaining how `near` composes with limit for 'the 10 closest', how `where`/`order_by`/`offset` interact, and how `datasets`/`category`+`total` merge into one priced answer. It does not, however, add meaning for several heavy params (permits, area_where, area_columns, shape) beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource — return matching rows from one dataset, filtered on any column, with distance search, sorting and paging — and names the sibling tools it is not (get_dataset, count_locations, create_query_checkout, buy_dataset). An agent can distinguish it from every sibling without opening a schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicit routing guidance: get_dataset lists columns, count_locations (free) tells how many rows match and what fetching costs, call without payment first to get an x402 quote, and pass datasets/category+total instead of dataset when covering several chains. When-to-use and when-to-use-something-else are both spelled out.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Free. How one set of places relates to another, by straight-line distance. The base set is the usual dataset / datasets / category plus filters; relate.anchor is the other set, given the same way. Modes: nearest (each base row's k<=3 nearest anchors with miles), count_within (rank base rows by how many anchors are within radius_miles), within_any (base rows with at least one anchor within radius_miles), none_within (base rows with no anchor within radius_miles). Mode next_best (no anchor, needs the base set's state) ranks the candidate NEW sites in that state by how well they match what the base list's own locations typically have nearby, blended with an estimated market capture; every candidate names its matched factors. Mode typically_near answers "who is X typically located near?" — the PROFILE: the kinds of place and brands the base list's locations have nearby far more often than a typical spot (share of locations, lift, average miles), with an answer sentence and a link to the ranked sites; state optional (without one it is computed in the list's top state and the answer says which); a thin sample is said, never sold. Every set (base, anchor) also takes metro (a CBSA code or name) or areas_in (county / ZIP / metro / state ids), like count_locations. Mode near / not_near matches by distance only (near_m metres, default 30) and is how a LIVE public register is compared: give a set as opendata {source, state} instead of a dataset — search_datasets with kind "register" finds the source key. A register is read at the moment you ask, never sold; the answer names its publisher, licence and read time. Returns counts for both sets (rows without coordinates are left out and counted), summary stats, up to 3 preview rows, the price of the full answer (base rows plus the anchor rows named, each at its dataset's per-row rate, one card fee) and how to buy it with query_locations or create_query_checkout using the same arguments. Example: {"dataset": "", "state": "VA", "relate": {"mode": "nearest", "k": 1, "anchor": {"dataset": "", "metro": "Richmond"}}}. Profile: {"dataset": "", "relate": {"mode": "typically_near"}}. Live register: {"dataset": "ymca", "state": "NY", "relate": {"mode": "near", "near_m": 30, "anchor": {"opendata": {"source": "data.ny.gov/cb42-qumz", "state": "NY"}}}}.
| Name | Required | Description | Default |
|---|---|---|---|
| zip | No | ||
| city | No | ||
| near | No | Distance search on lists with coordinates: ONE of place, zip, lat+lng or points, with radius_miles or drive_minutes (not both). | |
| metro | No | Only rows in one metro area (CBSA): its 5-digit code ("37980") or its name ("Philadelphia", "Philadelphia, PA"). The same as areas_in {"by": "metro", "ids": [code]}. | |
| state | No | ||
| total | No | Base rows wanted, first in answer order | |
| where | No | ||
| county | No | ||
| relate | Yes | Relate each base row to an anchor set. nearest: the k nearest anchors with miles. count_within: rank by anchors within radius_miles. within_any: rows with an anchor within radius_miles. none_within: rows with none. same_place / not_same_place: base rows that are (or are not) the same physical place as a row of each set — same address, or within 60 m sharing a name word — with every region of the overlap counted; N-way with anchors, also and not_also. near / not_near: the same N-way shape matched by DISTANCE ONLY, within near_m metres (default 30) — the rule for a live open-data register given as a set's opendata. next_best: no anchor; needs the base set's state — the candidate sites in that state ranked by how well they match what the base list's own locations typically have nearby, blended with an estimated market capture. typically_near: the PROFILE of the same analysis, no anchor, state optional — what the base list's locations typically have nearby (the signature: each kind of place or brand near at least 15% of them and at least 1.5x as often as near a typical commercial spot), with a link to the ranked sites; without a state it is computed in the list's top state and the answer says which. Under 10 locations the pattern is shown with a thin-sample note and nothing is sold. Straight-line miles, or real drive time with within_drive_minutes (5-60) on count_within / within_any / none_within, capped at 60 base rows. overlap: a TERRITORY question, not a row count — buffer every base row and every anchor row by radius_miles (straight-line, default 3, 1-25), union each into one shape, and answer what share of the base's shape the anchor's shape covers, plus each base row's own share (lowest first finds the whitespace rows with no nearby anchor territory). Free: the shares, the two territories in square miles and the headline percentage. Paid: the base rows with their own share, at that list's per-row rate. | |
| dataset | No | ||
| exclude | No | Dataset slugs left out wherever the set expands: a category minus one of its members (a list's competitors are its own category with itself excluded). | |
| areas_in | No | Only rows in these areas, e.g. {"by": "county", "ids": ["51760"]} for Richmond city, VA — the ids count_by_area returns, placed the same way (/find: areas_in=county:51760). | |
| category | No | ||
| datasets | No | ||
| opendata | No | INSTEAD of dataset / datasets / category: a public register read LIVE from the body that publishes it, at the moment of the question — e.g. a state's licensed child-care programs. Matched by distance only (relate.mode near / not_near, near_m metres, default 30), because the publisher's own coordinates are the evidence. It is not a list we sell: the answer gives counts and a preview, names the publisher, the licence and when it was read, and prices only the rows of OUR lists. E.g. {"dataset": "ymca", "state": "NY", "relate": {"mode": "near", "near_m": 30, "anchor": {"opendata": {"source": "data.ny.gov/cb42-qumz", "state": "NY"}}}}. | |
| area_where | No | Only rows whose county/zip/state/metro meets a Census condition, each as "<kind>:<attribute><op><value>" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. "county:population>1000000". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 191 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …. | |
| area_columns | No | Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as "<kind>:<attribute>,<attribute>", e.g. "county:population,median_household_income" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy, after the area's id and name (county_fips, county_name; zip_code; metro_cbsa, metro_name; state_code, state_name). Free. The same attribute words area_where takes (listed there). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds rich behavior beyond the annotations: which answers are free vs paid and the pricing mechanics ('base rows plus the anchor rows named, each at its dataset's per-row rate, one card fee'), that registers are read live at query time and the answer names publisher/licence/read time, thin-sample handling ('a thin sample is said, never sold'), and the drive-time-unavailable fallback to radius_miles. With readOnly/idempotent/openWorld already declared, this is substantial extra context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Front-loaded with 'Free.' and the core one-line purpose, and the mode walk-through is ordered. But the body is a dense wall of run-on sentences mixing modes, pricing, examples and opendata caveats, so signal is hard to extract despite the length being partly justified by tool complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a highly complex, 17-param, deeply nested tool with no output schema, it documents the return shape ('counts for both sets ... summary stats, up to 3 preview rows, the price of the full answer'), the cost model and purchase path, and gives three concrete request examples. Only a few secondary parameters are left unexplained, keeping it just short of complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is only 53% across 17 params, so the description must carry weight — and it does for the important ones: it explains relate.mode semantics, relate.anchor as the second set, opendata vs dataset, metro/areas_in placement, and gives worked JSON examples. It leaves several params (also, not_also, anchor_all, lambda, reveal_anchor, area_where/area_columns) largely to the schema, so not a 5.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Opens with a specific verb+resource+method: 'How one set of places relates to another, by straight-line distance,' then enumerates every mode (nearest, count_within, within_any, none_within, next_best, typically_near, near/not_near). It distinguishes itself from adjacent tools by referencing count_locations, search_datasets and query_locations, so an agent can tell what this tool uniquely does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Gives clear conditional guidance per mode (e.g. 'near / not_near ... is how a LIVE public register is compared', 'Mode next_best ... ranks the candidate NEW sites', 'typically_near answers who is X typically located near'). It routes the agent to siblings for supporting steps ('search_datasets with kind "register" finds the source key', 'how to buy it with query_locations or create_query_checkout'). It stops short of an explicit 'use this instead of cotenancy when...' exclusion, so not a full 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Ask LocationLists to add a list we do not have yet. Use it when search_datasets finds nothing that fits, or the user wants a brand, place or kind of business we do not publish. BEFORE calling: ask the user whether to send the request, and ask for their email so we can tell them when the list is ready. Pass an email only if the user gave it to you in this conversation; never guess or invent one. Called with neither email nor email_declined, it sends nothing and asks for the email. The request goes to the LocationLists team, the same place as the request box on locationlists.com. We add new datasets every day and prioritize requested ones; there is no promised date. Free, nothing is charged.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | The user's name, if they gave it | |
| No | The user's email, only if they gave it, so we can tell them when the list is ready | ||
| topic | Yes | The list the user wants, in their words (2-200 characters) | |
| company | No | The user's company, if they gave it | |
| details | No | Anything else they specified: places, fields needed (phone, website, email), timing | |
| use_case | No | What they will use the list for, if they said (e.g. sales outreach, territory planning) | |
| email_declined | No | True only when you asked the user for their email and they chose not to leave one |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations only tell the agent this is a non-idempotent, non-destructive write to an open world. The description adds real context beyond that: the request routes to the LocationLists team (same as the site's request box), there is no promised date, and the call is free with nothing charged. It also discloses the operational side effect that a call with neither email nor email_declined sends nothing and prompts for the email.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The purpose and trigger condition are front-loaded in the first two sentences, and the pre-call checklist follows before the less critical detail about routing and cost. The final sentence ('Free, nothing is charged') is arguably redundant with the free-service framing, so it is slightly longer than strictly necessary.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 7-parameter, non-idempotent, open-world write with no output schema, the description covers the trigger, the confirmation protocol, the data-integrity guardrail on email, and the expected side effect. The only unimplemented piece is what the agent receives back after a successful send, which is minor given the tool's request-submission nature.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so every parameter including email_declined already carries its own field-level documentation. The description largely restates those rules (pass email only if given, never invent one; called with neither email nor email_declined sends nothing) rather than adding new per-parameter meaning, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific verb and resource ('Ask LocationLists to add a list we do not have yet') and immediately distinguishes this tool from the data-retrieval siblings by naming search_datasets as the trigger condition. An agent can tell this apart from search_datasets or buy_dataset without opening any schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives an explicit triggering condition ('when search_datasets finds nothing that fits, or the user wants a brand, place or kind of business we do not publish') and an explicit pre-call protocol: ask the user whether to send the request and ask for their email. It also states the alternative behavior when no email decision is supplied, so the agent knows what happens next.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Find LocationLists datasets by brand, kind of business or industry (e.g. 'bobcat', 'restaurants', 'bank branches', 'dental practices', 'hardware stores'). Returns EVERY matching dataset, best first, with slug, name, business type, industry, record count, coverage, whether it can be searched by distance (distanceSearch), and page URL. A kind of business or an industry in the query matches every dataset of that kind, and kinds names it as a category that count_locations can combine into one answer. Each brand or chain is its own dataset. After finding one you can filter it by city, state, zip, any column, or a radius around a place (e.g. within 25 miles of Los Angeles, CA) when it has coordinates: use count_locations for how many match, and get_sample with the same filters for that count plus the list's fixed free sample rows, each marked matches_your_question. Both are free. To cover several chains near one place, pass datasets or category and a total to count_locations, and you get one answer, one price and one file. kind "register" searches LIVE public registers instead (a state's licensed child care, liquor licences, SNAP retailers…): each result's source is the key relate_locations takes as a set's opendata {source, state}, compared by distance (mode near). Registers are read live, never sold.
| Name | Required | Description | Default |
|---|---|---|---|
| kind | No | list (default): the lists we sell. register: live public registers read from their publisher, e.g. {"query": "child care", "kind": "register", "state": "NY"} — each result's source goes in relate_locations as {"opendata": {"source": "<source>", "state": "NY"}}. | |
| limit | No | Max results (default: every match) | |
| query | No | Free text: brand, kind of business, industry or product | |
| state | No | With kind "register": prefer registers covering this state, e.g. "NY" | |
| category | No | Restrict to one catalog category, industry or subcategory |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly/idempotent/openWorld/non-destructive, so the safety profile is covered. Beyond that the description adds real behavioral context: results come back complete and best-first with a listed field set, the operations are free ('Both are free'), registers are read live and 'never sold', and each brand is treated as its own dataset. These are useful traits not present in the annotations, though some of the text reads more as routing advice than behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The first sentence is well front-loaded, but the remainder is one sprawling paragraph that crams list-mode, register-mode, distance filtering, pricing and multi-chain aggregation together. It is dense and fatiguing and would read far better as bullets, though little of it is strictly redundant.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
There is no output schema, so the description usefully enumerates the returned fields (slug, name, business type, industry, record count, coverage, distanceSearch, page URL) and result ordering. Combined with the register/live-read caveat and sibling routing, an agent has nearly everything needed, with only the minor `kinds`/`category` ambiguity outstanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already documents all five parameters and the baseline is 3. The description reinforces the 'register' semantics of `kind`/`state` and cross-tool use of the result's `source`, but it inconsistently refers to a `kinds` category concept that appears in the schema as `category`, which risks mild confusion rather than adding clean meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Opens with a specific verb+resource ('Find LocationLists datasets'), scopes it by brand/kind/industry with concrete examples, and immediately distinguishes the 'list' vs 'register' modes. It also names the sibling it hands off to (count_locations, get_sample), so an agent can tell it apart from the rest of the family without opening a schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Gives explicit hand-off conditions: count_locations when you want a count, get_sample for count plus fixed free rows, and passing datasets/category with a total to combine chains into one answer/price/file. The register path is spelled out as a separate query mode. Nothing about when to reach here vs a sibling is left to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Send a message to the LocationLists team: wrong or missing data in a dataset, something that did not work, a pricing question, an idea, or anything else. Ask the user before sending and use their words. Ask for their email so the team can reply, and pass it only if they gave it; never guess or invent one. Called with neither email nor email_declined, it sends nothing and asks for the email. Free.
| Name | Required | Description | Default |
|---|---|---|---|
| No | The user's email, only if they gave it | ||
| dataset | No | Dataset slug it concerns, if any, e.g. generac-dealers | |
| message | Yes | The feedback, in the user's words (5-4000 characters) | |
| category | No | What it is about (default other) | |
| email_declined | No | True only when you asked the user for their email and they chose not to leave one |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations only declare openWorld/non-readOnly/non-idempotent; the description goes well beyond by mandating user confirmation before sending, forbidding invented emails, documenting the no-op path when neither email nor email_declined is supplied, and noting the call is free.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Front-loads the purpose, then layers the confirmation and email rules; each sentence carries distinct behavioral information. The first sentence's topic list is long but serves routing, so it earns its space.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a non-idempotent outbound-message tool with no output schema, the description closes the gaps that matter: consent requirement, privacy handling of the email field, and the exact behavior of the degenerate call. Nothing needed to invoke it correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3, but the description adds real semantics the schema cannot: email must only be passed if the user actually gave one, and email_declined is meaningful only in relation to a missing email (neither present means nothing is sent).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb (send) and destination (the LocationLists team) plus the full range of content it accepts (bad data, breakage, pricing, ideas). It is unmistakably distinct from every sibling, which are all dataset/query/order tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The opening list enumerates concrete triggers for use, and it adds a workflow precondition ('Ask the user before sending and use their words'). It does not name a when-not-to-use case or an alternative route, but no sibling overlaps functionally.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
cotenancy2 fields changedInput schema / properties / a / properties / area_columns / descriptionPrevious value: -"Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy. Free. The same attribute words area_where takes (listed there)."New value: +"Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy, after the area's id and name (county_fips, county_name; zip_code; metro_cbsa, metro_name; state_code, state_name). Free. The same attribute words area_where takes (listed there)."Input schema / properties / a / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 191 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …."count_by_area3 fields changedInput schema / properties / area_where / descriptionPrevious value: -"Only areas meeting a Census condition, each as \"<kind>:<attribute><op><value>\" about the same kind as by (or a state), e.g. \"county:population>500000\" with lacks for \"counties over 500k people with no X\". Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …."New value: +"Only areas meeting a Census condition, each as \"<kind>:<attribute><op><value>\" about the same kind as by (or a state), e.g. \"county:population>500000\" with lacks for \"counties over 500k people with no X\". Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 191 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …."Input schema / properties / sets / items / properties / area_columns / descriptionPrevious value: -"Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy. Free. The same attribute words area_where takes (listed there)."New value: +"Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy, after the area's id and name (county_fips, county_name; zip_code; metro_cbsa, metro_name; state_code, state_name). Free. The same attribute words area_where takes (listed there)."Input schema / properties / sets / items / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 191 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …."count_locations2 fields changedInput schema / properties / area_columns / descriptionPrevious value: -"Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy. Free. The same attribute words area_where takes (listed there)."New value: +"Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy, after the area's id and name (county_fips, county_name; zip_code; metro_cbsa, metro_name; state_code, state_name). Free. The same attribute words area_where takes (listed there)."Input schema / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 191 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …."create_query_checkout2 fields changedInput schema / properties / area_columns / descriptionPrevious value: -"Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy. Free. The same attribute words area_where takes (listed there)."New value: +"Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy, after the area's id and name (county_fips, county_name; zip_code; metro_cbsa, metro_name; state_code, state_name). Free. The same attribute words area_where takes (listed there)."Input schema / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 191 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …."email_quote2 fields changedInput schema / properties / area_columns / descriptionPrevious value: -"Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy. Free. The same attribute words area_where takes (listed there)."New value: +"Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy, after the area's id and name (county_fips, county_name; zip_code; metro_cbsa, metro_name; state_code, state_name). Free. The same attribute words area_where takes (listed there)."Input schema / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 191 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …."get_sample2 fields changedInput schema / properties / area_columns / descriptionPrevious value: -"Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy. Free. The same attribute words area_where takes (listed there)."New value: +"Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy, after the area's id and name (county_fips, county_name; zip_code; metro_cbsa, metro_name; state_code, state_name). Free. The same attribute words area_where takes (listed there)."Input schema / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 191 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …."query_locations2 fields changedInput schema / properties / area_columns / descriptionPrevious value: -"Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy. Free. The same attribute words area_where takes (listed there)."New value: +"Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy, after the area's id and name (county_fips, county_name; zip_code; metro_cbsa, metro_name; state_code, state_name). Free. The same attribute words area_where takes (listed there)."Input schema / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 191 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …."relate_locations2 fields changedInput schema / properties / area_columns / descriptionPrevious value: -"Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy. Free. The same attribute words area_where takes (listed there)."New value: +"Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy, after the area's id and name (county_fips, county_name; zip_code; metro_cbsa, metro_name; state_code, state_name). Free. The same attribute words area_where takes (listed there)."Input schema / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 191 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …."cotenancy7 fields changedInput schema / properties / a / properties / areas_inAdded value: +{
+ "additionalProperties": false,
+ "description": "Only rows in these areas, e.g. {\"by\": \"county\", \"ids\": [\"51760\"]} for Richmond city, VA — the ids count_by_area returns, placed the same way (/find: areas_in=county:51760).",
+ "properties": {
+ "by": {
+ "enum": [
+ "county",
+ "zip",
+ "state",
+ "metro"
+ ],
+ "type": "string"
+ },
+ "ids": {
+ "description": "County FIPS, 5-digit ZIP, CBSA code or state code — the `id` of a count_by_area row",
+ "items": {
+ "type": "string"
+ },
+ "maxItems": 200,
+ "minItems": 1,
+ "type": "array"
+ }
+ },
+ "required": [
+ "by",
+ "ids"
+ ],
+ "type": "object"
+}Input schema / properties / a / properties / metroAdded value: +{
+ "description": "Only rows in one metro area (CBSA): its 5-digit code (\"37980\") or its name (\"Philadelphia\", \"Philadelphia, PA\"). The same as areas_in {\"by\": \"metro\", \"ids\": [code]}.",
+ "type": "string"
+}Input schema / properties / a / properties / near / properties / drive_minutes / descriptionPrevious value: -"Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free)."New value: +"Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free). Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free)."Input schema / properties / a / properties / opendataAdded value: +{
+ "additionalProperties": false,
+ "description": "INSTEAD of dataset / datasets / category: a public register read LIVE from the body that publishes it, at the moment of the question — e.g. a state's licensed child-care programs. Matched by distance only (relate.mode near / not_near, near_m metres, default 30), because the publisher's own coordinates are the evidence. It is not a list we sell: the answer gives counts and a preview, names the publisher, the licence and when it was read, and prices only the rows of OUR lists. E.g. {\"dataset\": \"ymca\", \"state\": \"NY\", \"relate\": {\"mode\": \"near\", \"near_m\": 30, \"anchor\": {\"opendata\": {\"source\": \"data.ny.gov/cb42-qumz\", \"state\": \"NY\"}}}}.",
+ "properties": {
+ "source": {
+ "description": "The register's key, \"<domain>/<id>\" — search_datasets with kind \"register\" finds it, e.g. \"data.ny.gov/cb42-qumz\" (New York's licensed child-care programs)",
+ "type": "string"
+ },
+ "state": {
+ "description": "Cut the register to one state, e.g. \"NY\" (/find: opendata_state=NY)",
+ "type": "string"
+ },
+ "where": {
+ "description": "Conditions on the register's own columns, each \"column:op:value\" with op eq | starts_with | contains | not_blank, e.g. [\"license_status:starts_with:Active\"] (/find: opendata_where=)",
+ "items": {
+ "type": "string"
+ },
+ "maxItems": 12,
+ "type": "array"
+ },
+ "zips": {
+ "description": "Or cut it to 5-digit ZIP codes, e.g. [\"78114\", \"78154\"] (/find: opendata_zips=78114,78154)",
+ "items": {
+ "type": "string"
+ },
+ "minItems": 1,
+ "type": "array"
+ }
+ },
+ "required": [
+ "source"
+ ],
+ "type": "object"
+}Input schema / properties / b / properties / areas_inAdded value: +{
+ "$ref": "#/properties/a/properties/areas_in"
+}Input schema / properties / b / properties / metroAdded value: +{
+ "$ref": "#/properties/a/properties/metro"
+}Input schema / properties / b / properties / opendataAdded value: +{
+ "$ref": "#/properties/a/properties/opendata",
+ "description": "INSTEAD of dataset / datasets / category: a public register read LIVE from the body that publishes it, at the moment of the question — e.g. a state's licensed child-care programs. Matched by distance only (relate.mode near / not_near, near_m metres, default 30), because the publisher's own coordinates are the evidence. It is not a list we sell: the answer gives counts and a preview, names the publisher, the licence and when it was read, and prices only the rows of OUR lists. E.g. {\"dataset\": \"ymca\", \"state\": \"NY\", \"relate\": {\"mode\": \"near\", \"near_m\": 30, \"anchor\": {\"opendata\": {\"source\": \"data.ny.gov/cb42-qumz\", \"state\": \"NY\"}}}}."
+}count_by_area4 fields changedInput schema / properties / sets / items / properties / areas_inAdded value: +{
+ "additionalProperties": false,
+ "description": "Only rows in these areas, e.g. {\"by\": \"county\", \"ids\": [\"51760\"]} for Richmond city, VA — the ids count_by_area returns, placed the same way (/find: areas_in=county:51760).",
+ "properties": {
+ "by": {
+ "enum": [
+ "county",
+ "zip",
+ "state",
+ "metro"
+ ],
+ "type": "string"
+ },
+ "ids": {
+ "description": "County FIPS, 5-digit ZIP, CBSA code or state code — the `id` of a count_by_area row",
+ "items": {
+ "type": "string"
+ },
+ "maxItems": 200,
+ "minItems": 1,
+ "type": "array"
+ }
+ },
+ "required": [
+ "by",
+ "ids"
+ ],
+ "type": "object"
+}Input schema / properties / sets / items / properties / metroAdded value: +{
+ "description": "Only rows in one metro area (CBSA): its 5-digit code (\"37980\") or its name (\"Philadelphia\", \"Philadelphia, PA\"). The same as areas_in {\"by\": \"metro\", \"ids\": [code]}.",
+ "type": "string"
+}Input schema / properties / sets / items / properties / near / properties / drive_minutes / descriptionPrevious value: -"Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free)."New value: +"Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free). Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free)."Input schema / properties / sets / items / properties / opendataAdded value: +{
+ "additionalProperties": false,
+ "description": "INSTEAD of dataset / datasets / category: a public register read LIVE from the body that publishes it, at the moment of the question — e.g. a state's licensed child-care programs. Matched by distance only (relate.mode near / not_near, near_m metres, default 30), because the publisher's own coordinates are the evidence. It is not a list we sell: the answer gives counts and a preview, names the publisher, the licence and when it was read, and prices only the rows of OUR lists. E.g. {\"dataset\": \"ymca\", \"state\": \"NY\", \"relate\": {\"mode\": \"near\", \"near_m\": 30, \"anchor\": {\"opendata\": {\"source\": \"data.ny.gov/cb42-qumz\", \"state\": \"NY\"}}}}.",
+ "properties": {
+ "source": {
+ "description": "The register's key, \"<domain>/<id>\" — search_datasets with kind \"register\" finds it, e.g. \"data.ny.gov/cb42-qumz\" (New York's licensed child-care programs)",
+ "type": "string"
+ },
+ "state": {
+ "description": "Cut the register to one state, e.g. \"NY\" (/find: opendata_state=NY)",
+ "type": "string"
+ },
+ "where": {
+ "description": "Conditions on the register's own columns, each \"column:op:value\" with op eq | starts_with | contains | not_blank, e.g. [\"license_status:starts_with:Active\"] (/find: opendata_where=)",
+ "items": {
+ "type": "string"
+ },
+ "maxItems": 12,
+ "type": "array"
+ },
+ "zips": {
+ "description": "Or cut it to 5-digit ZIP codes, e.g. [\"78114\", \"78154\"] (/find: opendata_zips=78114,78154)",
+ "items": {
+ "type": "string"
+ },
+ "minItems": 1,
+ "type": "array"
+ }
+ },
+ "required": [
+ "source"
+ ],
+ "type": "object"
+}count_locations3 fields changedInput schema / properties / near / descriptionPrevious value: -"Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, lat+lng, or `points` (several of those: within reach of ANY of them, distance to the nearest). Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes; 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted."New value: +"Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, lat+lng, or `points` (several of those: within reach of ANY of them, distance to the nearest). Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes; 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted. Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free)."Input schema / properties / near / properties / drive_minutes / descriptionPrevious value: -"Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free)."New value: +"Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free). Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free)."Input schema / properties / permitsAdded value: +{
+ "additionalProperties": false,
+ "description": "Only rows with a BUILDING PERMIT nearby, read live from the city's own permit register at question time — e.g. {\"dataset\": \"<slug>\", \"city\": \"Austin\", \"state\": \"TX\", \"permits\": {\"days\": 90, \"type\": \"commercial\", \"min_usd\": 1000000}} for places near a commercial permit issued in the last 90 days valued at $1M or more. days: issued in the last N days (default 365, and the answer says the window was defaulted); type: one of any, new_construction, commercial, residential, renovation, demolition, electrical, plumbing, mechanical, roofing, solar, sign, pool; min_usd: the valuation floor; metres: how close a permit must be (default 60). One city register per question, chosen from the base set's city, a ZIP in it, its county, or a state with one register; the cities read now: New York, NY; Los Angeles, CA; Chicago, IL; Dallas, TX; Austin, TX; San Francisco, CA; Seattle, WA; Nashville, TN. A kind or amount a city's register cannot tell is refused in words, never answered as any. The answer is free (counts, a preview, the price of the matching rows of our list) and its openData block names the register, its publisher, licence and when it was read. The same question on /find: …&permits=1&permits_days=90&permits_type=commercial&permits_min_usd=1000000. Not sold through a checkout yet: the answer's link is the page.",
+ "properties": {
+ "days": {
+ "description": "Issued in the last N days (default 365). /find: permits_days=90",
+ "maximum": 3650,
+ "minimum": 1,
+ "type": "integer"
+ },
+ "metres": {
+ "description": "How close a permit must be to the row, in metres (default 60). /find: permits_m=60",
+ "maximum": 200,
+ "minimum": 10,
+ "type": "integer"
+ },
+ "min_usd": {
+ "description": "Valuation at least this many dollars, e.g. 1000000. /find: permits_min_usd=1000000",
+ "minimum": 0,
+ "type": "number"
+ },
+ "type": {
+ "description": "The kind of permit (default any). /find: permits_type=commercial",
+ "enum": [
+ "any",
+ "new_construction",
+ "commercial",
+ "residential",
+ "renovation",
+ "demolition",
+ "electrical",
+ "plumbing",
+ "mechanical",
+ "roofing",
+ "solar",
+ "sign",
+ "pool"
+ ],
+ "type": "string"
+ }
+ },
+ "type": "object"
+}create_query_checkout3 fields changedInput schema / properties / near / descriptionPrevious value: -"Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, lat+lng, or `points` (several of those: within reach of ANY of them, distance to the nearest). Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes; 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted."New value: +"Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, lat+lng, or `points` (several of those: within reach of ANY of them, distance to the nearest). Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes; 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted. Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free)."Input schema / properties / near / properties / drive_minutes / descriptionPrevious value: -"Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free)."New value: +"Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free). Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free)."Input schema / properties / permitsAdded value: +{
+ "additionalProperties": false,
+ "description": "Only rows with a BUILDING PERMIT nearby, read live from the city's own permit register at question time — e.g. {\"dataset\": \"<slug>\", \"city\": \"Austin\", \"state\": \"TX\", \"permits\": {\"days\": 90, \"type\": \"commercial\", \"min_usd\": 1000000}} for places near a commercial permit issued in the last 90 days valued at $1M or more. days: issued in the last N days (default 365, and the answer says the window was defaulted); type: one of any, new_construction, commercial, residential, renovation, demolition, electrical, plumbing, mechanical, roofing, solar, sign, pool; min_usd: the valuation floor; metres: how close a permit must be (default 60). One city register per question, chosen from the base set's city, a ZIP in it, its county, or a state with one register; the cities read now: New York, NY; Los Angeles, CA; Chicago, IL; Dallas, TX; Austin, TX; San Francisco, CA; Seattle, WA; Nashville, TN. A kind or amount a city's register cannot tell is refused in words, never answered as any. The answer is free (counts, a preview, the price of the matching rows of our list) and its openData block names the register, its publisher, licence and when it was read. The same question on /find: …&permits=1&permits_days=90&permits_type=commercial&permits_min_usd=1000000. Not sold through a checkout yet: the answer's link is the page.",
+ "properties": {
+ "days": {
+ "description": "Issued in the last N days (default 365). /find: permits_days=90",
+ "maximum": 3650,
+ "minimum": 1,
+ "type": "integer"
+ },
+ "metres": {
+ "description": "How close a permit must be to the row, in metres (default 60). /find: permits_m=60",
+ "maximum": 200,
+ "minimum": 10,
+ "type": "integer"
+ },
+ "min_usd": {
+ "description": "Valuation at least this many dollars, e.g. 1000000. /find: permits_min_usd=1000000",
+ "minimum": 0,
+ "type": "number"
+ },
+ "type": {
+ "description": "The kind of permit (default any). /find: permits_type=commercial",
+ "enum": [
+ "any",
+ "new_construction",
+ "commercial",
+ "residential",
+ "renovation",
+ "demolition",
+ "electrical",
+ "plumbing",
+ "mechanical",
+ "roofing",
+ "solar",
+ "sign",
+ "pool"
+ ],
+ "type": "string"
+ }
+ },
+ "type": "object"
+}email_quote3 fields changedInput schema / properties / near / descriptionPrevious value: -"Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, lat+lng, or `points` (several of those: within reach of ANY of them, distance to the nearest). Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes; 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted."New value: +"Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, lat+lng, or `points` (several of those: within reach of ANY of them, distance to the nearest). Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes; 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted. Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free)."Input schema / properties / near / properties / drive_minutes / descriptionPrevious value: -"Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free)."New value: +"Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free). Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free)."Input schema / properties / permitsAdded value: +{
+ "additionalProperties": false,
+ "description": "Only rows with a BUILDING PERMIT nearby, read live from the city's own permit register at question time — e.g. {\"dataset\": \"<slug>\", \"city\": \"Austin\", \"state\": \"TX\", \"permits\": {\"days\": 90, \"type\": \"commercial\", \"min_usd\": 1000000}} for places near a commercial permit issued in the last 90 days valued at $1M or more. days: issued in the last N days (default 365, and the answer says the window was defaulted); type: one of any, new_construction, commercial, residential, renovation, demolition, electrical, plumbing, mechanical, roofing, solar, sign, pool; min_usd: the valuation floor; metres: how close a permit must be (default 60). One city register per question, chosen from the base set's city, a ZIP in it, its county, or a state with one register; the cities read now: New York, NY; Los Angeles, CA; Chicago, IL; Dallas, TX; Austin, TX; San Francisco, CA; Seattle, WA; Nashville, TN. A kind or amount a city's register cannot tell is refused in words, never answered as any. The answer is free (counts, a preview, the price of the matching rows of our list) and its openData block names the register, its publisher, licence and when it was read. The same question on /find: …&permits=1&permits_days=90&permits_type=commercial&permits_min_usd=1000000. Not sold through a checkout yet: the answer's link is the page.",
+ "properties": {
+ "days": {
+ "description": "Issued in the last N days (default 365). /find: permits_days=90",
+ "maximum": 3650,
+ "minimum": 1,
+ "type": "integer"
+ },
+ "metres": {
+ "description": "How close a permit must be to the row, in metres (default 60). /find: permits_m=60",
+ "maximum": 200,
+ "minimum": 10,
+ "type": "integer"
+ },
+ "min_usd": {
+ "description": "Valuation at least this many dollars, e.g. 1000000. /find: permits_min_usd=1000000",
+ "minimum": 0,
+ "type": "number"
+ },
+ "type": {
+ "description": "The kind of permit (default any). /find: permits_type=commercial",
+ "enum": [
+ "any",
+ "new_construction",
+ "commercial",
+ "residential",
+ "renovation",
+ "demolition",
+ "electrical",
+ "plumbing",
+ "mechanical",
+ "roofing",
+ "solar",
+ "sign",
+ "pool"
+ ],
+ "type": "string"
+ }
+ },
+ "type": "object"
+}get_sample3 fields changedInput schema / properties / near / descriptionPrevious value: -"Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, lat+lng, or `points` (several of those: within reach of ANY of them, distance to the nearest). Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes; 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted."New value: +"Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, lat+lng, or `points` (several of those: within reach of ANY of them, distance to the nearest). Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes; 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted. Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free)."Input schema / properties / near / properties / drive_minutes / descriptionPrevious value: -"Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free)."New value: +"Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free). Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free)."Input schema / properties / permitsAdded value: +{
+ "additionalProperties": false,
+ "description": "Only rows with a BUILDING PERMIT nearby, read live from the city's own permit register at question time — e.g. {\"dataset\": \"<slug>\", \"city\": \"Austin\", \"state\": \"TX\", \"permits\": {\"days\": 90, \"type\": \"commercial\", \"min_usd\": 1000000}} for places near a commercial permit issued in the last 90 days valued at $1M or more. days: issued in the last N days (default 365, and the answer says the window was defaulted); type: one of any, new_construction, commercial, residential, renovation, demolition, electrical, plumbing, mechanical, roofing, solar, sign, pool; min_usd: the valuation floor; metres: how close a permit must be (default 60). One city register per question, chosen from the base set's city, a ZIP in it, its county, or a state with one register; the cities read now: New York, NY; Los Angeles, CA; Chicago, IL; Dallas, TX; Austin, TX; San Francisco, CA; Seattle, WA; Nashville, TN. A kind or amount a city's register cannot tell is refused in words, never answered as any. The answer is free (counts, a preview, the price of the matching rows of our list) and its openData block names the register, its publisher, licence and when it was read. The same question on /find: …&permits=1&permits_days=90&permits_type=commercial&permits_min_usd=1000000. Not sold through a checkout yet: the answer's link is the page.",
+ "properties": {
+ "days": {
+ "description": "Issued in the last N days (default 365). /find: permits_days=90",
+ "maximum": 3650,
+ "minimum": 1,
+ "type": "integer"
+ },
+ "metres": {
+ "description": "How close a permit must be to the row, in metres (default 60). /find: permits_m=60",
+ "maximum": 200,
+ "minimum": 10,
+ "type": "integer"
+ },
+ "min_usd": {
+ "description": "Valuation at least this many dollars, e.g. 1000000. /find: permits_min_usd=1000000",
+ "minimum": 0,
+ "type": "number"
+ },
+ "type": {
+ "description": "The kind of permit (default any). /find: permits_type=commercial",
+ "enum": [
+ "any",
+ "new_construction",
+ "commercial",
+ "residential",
+ "renovation",
+ "demolition",
+ "electrical",
+ "plumbing",
+ "mechanical",
+ "roofing",
+ "solar",
+ "sign",
+ "pool"
+ ],
+ "type": "string"
+ }
+ },
+ "type": "object"
+}query_locations3 fields changedInput schema / properties / near / descriptionPrevious value: -"Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, lat+lng, or `points` (several of those: within reach of ANY of them, distance to the nearest). Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes; 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted."New value: +"Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, lat+lng, or `points` (several of those: within reach of ANY of them, distance to the nearest). Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes; 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted. Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free)."Input schema / properties / near / properties / drive_minutes / descriptionPrevious value: -"Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free)."New value: +"Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free). Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free)."Input schema / properties / permitsAdded value: +{
+ "additionalProperties": false,
+ "description": "Only rows with a BUILDING PERMIT nearby, read live from the city's own permit register at question time — e.g. {\"dataset\": \"<slug>\", \"city\": \"Austin\", \"state\": \"TX\", \"permits\": {\"days\": 90, \"type\": \"commercial\", \"min_usd\": 1000000}} for places near a commercial permit issued in the last 90 days valued at $1M or more. days: issued in the last N days (default 365, and the answer says the window was defaulted); type: one of any, new_construction, commercial, residential, renovation, demolition, electrical, plumbing, mechanical, roofing, solar, sign, pool; min_usd: the valuation floor; metres: how close a permit must be (default 60). One city register per question, chosen from the base set's city, a ZIP in it, its county, or a state with one register; the cities read now: New York, NY; Los Angeles, CA; Chicago, IL; Dallas, TX; Austin, TX; San Francisco, CA; Seattle, WA; Nashville, TN. A kind or amount a city's register cannot tell is refused in words, never answered as any. The answer is free (counts, a preview, the price of the matching rows of our list) and its openData block names the register, its publisher, licence and when it was read. The same question on /find: …&permits=1&permits_days=90&permits_type=commercial&permits_min_usd=1000000. Not sold through a checkout yet: the answer's link is the page.",
+ "properties": {
+ "days": {
+ "description": "Issued in the last N days (default 365). /find: permits_days=90",
+ "maximum": 3650,
+ "minimum": 1,
+ "type": "integer"
+ },
+ "metres": {
+ "description": "How close a permit must be to the row, in metres (default 60). /find: permits_m=60",
+ "maximum": 200,
+ "minimum": 10,
+ "type": "integer"
+ },
+ "min_usd": {
+ "description": "Valuation at least this many dollars, e.g. 1000000. /find: permits_min_usd=1000000",
+ "minimum": 0,
+ "type": "number"
+ },
+ "type": {
+ "description": "The kind of permit (default any). /find: permits_type=commercial",
+ "enum": [
+ "any",
+ "new_construction",
+ "commercial",
+ "residential",
+ "renovation",
+ "demolition",
+ "electrical",
+ "plumbing",
+ "mechanical",
+ "roofing",
+ "solar",
+ "sign",
+ "pool"
+ ],
+ "type": "string"
+ }
+ },
+ "type": "object"
+}relate_locations20 fields changedInput schema / properties / areas_inAdded value: +{
+ "additionalProperties": false,
+ "description": "Only rows in these areas, e.g. {\"by\": \"county\", \"ids\": [\"51760\"]} for Richmond city, VA — the ids count_by_area returns, placed the same way (/find: areas_in=county:51760).",
+ "properties": {
+ "by": {
+ "enum": [
+ "county",
+ "zip",
+ "state",
+ "metro"
+ ],
+ "type": "string"
+ },
+ "ids": {
+ "description": "County FIPS, 5-digit ZIP, CBSA code or state code — the `id` of a count_by_area row",
+ "items": {
+ "type": "string"
+ },
+ "maxItems": 200,
+ "minItems": 1,
+ "type": "array"
+ }
+ },
+ "required": [
+ "by",
+ "ids"
+ ],
+ "type": "object"
+}Input schema / properties / metroAdded value: +{
+ "description": "Only rows in one metro area (CBSA): its 5-digit code (\"37980\") or its name (\"Philadelphia\", \"Philadelphia, PA\"). The same as areas_in {\"by\": \"metro\", \"ids\": [code]}.",
+ "type": "string"
+}Input schema / properties / near / properties / drive_minutes / descriptionPrevious value: -"Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free)."New value: +"Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free). Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free)."Input schema / properties / opendataAdded value: +{
+ "additionalProperties": false,
+ "description": "INSTEAD of dataset / datasets / category: a public register read LIVE from the body that publishes it, at the moment of the question — e.g. a state's licensed child-care programs. Matched by distance only (relate.mode near / not_near, near_m metres, default 30), because the publisher's own coordinates are the evidence. It is not a list we sell: the answer gives counts and a preview, names the publisher, the licence and when it was read, and prices only the rows of OUR lists. E.g. {\"dataset\": \"ymca\", \"state\": \"NY\", \"relate\": {\"mode\": \"near\", \"near_m\": 30, \"anchor\": {\"opendata\": {\"source\": \"data.ny.gov/cb42-qumz\", \"state\": \"NY\"}}}}.",
+ "properties": {
+ "source": {
+ "description": "The register's key, \"<domain>/<id>\" — search_datasets with kind \"register\" finds it, e.g. \"data.ny.gov/cb42-qumz\" (New York's licensed child-care programs)",
+ "type": "string"
+ },
+ "state": {
+ "description": "Cut the register to one state, e.g. \"NY\" (/find: opendata_state=NY)",
+ "type": "string"
+ },
+ "where": {
+ "description": "Conditions on the register's own columns, each \"column:op:value\" with op eq | starts_with | contains | not_blank, e.g. [\"license_status:starts_with:Active\"] (/find: opendata_where=)",
+ "items": {
+ "type": "string"
+ },
+ "maxItems": 12,
+ "type": "array"
+ },
+ "zips": {
+ "description": "Or cut it to 5-digit ZIP codes, e.g. [\"78114\", \"78154\"] (/find: opendata_zips=78114,78154)",
+ "items": {
+ "type": "string"
+ },
+ "minItems": 1,
+ "type": "array"
+ }
+ },
+ "required": [
+ "source"
+ ],
+ "type": "object"
+}Input schema / properties / relate / descriptionPrevious value: -"Relate each base row to an anchor set. nearest: the k nearest anchors with miles. count_within: rank by anchors within radius_miles. within_any: rows with an anchor within radius_miles. none_within: rows with none. same_place / not_same_place: base rows that are (or are not) the same physical place as a row of each set — same address, or within 60 m sharing a name word — with every region of the overlap counted; N-way with anchors, also and not_also. next_best: no anchor; needs the base set's state — the candidate sites in that state ranked by how well they match what the base list's own locations typically have nearby, blended with an estimated market capture. Straight-line miles, or real drive time with within_drive_minutes (5-60) on count_within / within_any / none_within, capped at 60 base rows. overlap: a TERRITORY question, not a row count — buffer every base row and every anchor row by radius_miles (straight-line, default 3, 1-25), union each into one shape, and answer what share of the base's shape the anchor's shape covers, plus each base row's own share (lowest first finds the whitespace rows with no nearby anchor territory). Free: the shares, the two territories in square miles and the headline percentage. Paid: the base rows with their own share, at that list's per-row rate."New value: +"Relate each base row to an anchor set. nearest: the k nearest anchors with miles. count_within: rank by anchors within radius_miles. within_any: rows with an anchor within radius_miles. none_within: rows with none. same_place / not_same_place: base rows that are (or are not) the same physical place as a row of each set — same address, or within 60 m sharing a name word — with every region of the overlap counted; N-way with anchors, also and not_also. near / not_near: the same N-way shape matched by DISTANCE ONLY, within near_m metres (default 30) — the rule for a live open-data register given as a set's opendata. next_best: no anchor; needs the base set's state — the candidate sites in that state ranked by how well they match what the base list's own locations typically have nearby, blended with an estimated market capture. typically_near: the PROFILE of the same analysis, no anchor, state optional — what the base list's locations typically have nearby (the signature: each kind of place or brand near at least 15% of them and at least 1.5x as often as near a typical commercial spot), with a link to the ranked sites; without a state it is computed in the list's top state and the answer says which. Under 10 locations the pattern is shown with a thin-sample note and nothing is sold. Straight-line miles, or real drive time with within_drive_minutes (5-60) on count_within / within_any / none_within, capped at 60 base rows. overlap: a TERRITORY question, not a row count — buffer every base row and every anchor row by radius_miles (straight-line, default 3, 1-25), union each into one shape, and answer what share of the base's shape the anchor's shape covers, plus each base row's own share (lowest first finds the whitespace rows with no nearby anchor territory). Free: the shares, the two territories in square miles and the headline percentage. Paid: the base rows with their own share, at that list's per-row rate."Input schema / properties / relate / properties / also / descriptionPrevious value: -"same_place only: the sets a base row must be the same place as some row of, by label (\"b\", \"c\"); default every set"New value: +"same_place / near only: the sets a base row must be the same place as (near: within near_m of) some row of, by label (\"b\", \"c\"); default every set"Input schema / properties / relate / properties / anchor / descriptionPrevious value: -"The other set: dataset, datasets or category, plus filters. Every mode but next_best needs it"New value: +"The other set: dataset, datasets or category, plus filters. Every mode but next_best and typically_near needs it"Input schema / properties / relate / properties / anchor / properties / areas_inAdded value: +{
+ "$ref": "#/properties/areas_in"
+}Input schema / properties / relate / properties / anchor / properties / metroAdded value: +{
+ "$ref": "#/properties/metro"
+}Input schema / properties / relate / properties / anchor / properties / opendataAdded value: +{
+ "$ref": "#/properties/opendata",
+ "description": "INSTEAD of dataset / datasets / category: a public register read LIVE from the body that publishes it, at the moment of the question — e.g. a state's licensed child-care programs. Matched by distance only (relate.mode near / not_near, near_m metres, default 30), because the publisher's own coordinates are the evidence. It is not a list we sell: the answer gives counts and a preview, names the publisher, the licence and when it was read, and prices only the rows of OUR lists. E.g. {\"dataset\": \"ymca\", \"state\": \"NY\", \"relate\": {\"mode\": \"near\", \"near_m\": 30, \"anchor\": {\"opendata\": {\"source\": \"data.ny.gov/cb42-qumz\", \"state\": \"NY\"}}}}."
+}Input schema / properties / relate / properties / anchors / descriptionPrevious value: -"same_place only: further sets (c, d) after anchor (b), each given the same way"New value: +"same_place (and near) only: further sets (c, d) after anchor (b), each given the same way"Input schema / properties / relate / properties / anchors / items / properties / areas_inAdded value: +{
+ "$ref": "#/properties/areas_in"
+}Input schema / properties / relate / properties / anchors / items / properties / metroAdded value: +{
+ "$ref": "#/properties/metro"
+}Input schema / properties / relate / properties / anchors / items / properties / opendataAdded value: +{
+ "$ref": "#/properties/opendata",
+ "description": "INSTEAD of dataset / datasets / category: a public register read LIVE from the body that publishes it, at the moment of the question — e.g. a state's licensed child-care programs. Matched by distance only (relate.mode near / not_near, near_m metres, default 30), because the publisher's own coordinates are the evidence. It is not a list we sell: the answer gives counts and a preview, names the publisher, the licence and when it was read, and prices only the rows of OUR lists. E.g. {\"dataset\": \"ymca\", \"state\": \"NY\", \"relate\": {\"mode\": \"near\", \"near_m\": 30, \"anchor\": {\"opendata\": {\"source\": \"data.ny.gov/cb42-qumz\", \"state\": \"NY\"}}}}."
+}Input schema / properties / relate / properties / k / descriptionPrevious value: -"nearest: how many anchors per base row. next_best: H3 rings counted as nearby, 1-3 (default 2, about a mile)"New value: +"nearest: how many anchors per base row. next_best / typically_near: H3 rings counted as nearby, 1-3 (default 2, about a mile)"Input schema / properties / relate / properties / mode / descriptionPrevious value: -"nearest | count_within | within_any | none_within | same_place | not_same_place | next_best | overlap"New value: +"nearest | count_within | within_any | none_within | same_place | not_same_place | near | not_near | next_best | typically_near | overlap"Input schema / properties / relate / properties / mode / enumPrevious value: -[
- "nearest",
- "count_within",
- "within_any",
- "none_within",
- "same_place",
- "not_same_place",
- "next_best",
- "overlap"
-]New value: +[
+ "nearest",
+ "count_within",
+ "within_any",
+ "none_within",
+ "same_place",
+ "not_same_place",
+ "near",
+ "not_near",
+ "next_best",
+ "typically_near",
+ "overlap"
+]Input schema / properties / relate / properties / near_mAdded value: +{
+ "description": "near / not_near only: how close, in metres, a place of the other set must be (default 30, 10-200). Distance only — no name or address is compared. /find: near_m=30.",
+ "maximum": 200,
+ "minimum": 10,
+ "type": "integer"
+}Input schema / properties / relate / properties / not_also / descriptionPrevious value: -"same_place only: the sets a base row must NOT be the same place as any row of; not_same_place with one set is [\"b\"]"New value: +"same_place / near only: the sets a base row must NOT match any row of; not_same_place (not_near) with one set is [\"b\"]"Input schema / properties / relate / properties / within_drive_minutes / descriptionPrevious value: -"Drive time instead of radius_miles (5-60 minutes). count_within / within_any / none_within only; at most 60 base rows."New value: +"Drive time instead of radius_miles (5-60 minutes). count_within / within_any / none_within only; at most 60 base rows. Drive time depends on an outside routing service: when an answer says drive-time search is unavailable or not switched on, ask the same question again with radius_miles (straight-line miles, always available, free)."search_datasets2 fields changedInput schema / properties / kindAdded value: +{
+ "description": "list (default): the lists we sell. register: live public registers read from their publisher, e.g. {\"query\": \"child care\", \"kind\": \"register\", \"state\": \"NY\"} — each result's source goes in relate_locations as {\"opendata\": {\"source\": \"<source>\", \"state\": \"NY\"}}.",
+ "enum": [
+ "list",
+ "register"
+ ],
+ "type": "string"
+}Input schema / properties / stateAdded value: +{
+ "description": "With kind \"register\": prefer registers covering this state, e.g. \"NY\"",
+ "type": "string"
+}cotenancy1 field changedInput schema / properties / a / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …."count_by_area2 fields changedInput schema / properties / area_where / descriptionPrevious value: -"Only areas meeting a Census condition, each as \"<kind>:<attribute><op><value>\" about the same kind as by (or a state), e.g. \"county:population>500000\" with lacks for \"counties over 500k people with no X\". Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …."New value: +"Only areas meeting a Census condition, each as \"<kind>:<attribute><op><value>\" about the same kind as by (or a state), e.g. \"county:population>500000\" with lacks for \"counties over 500k people with no X\". Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …."Input schema / properties / sets / items / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …."count_locations1 field changedInput schema / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …."create_query_checkout1 field changedInput schema / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …."email_quote1 field changedInput schema / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …."get_sample1 field changedInput schema / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …."query_locations1 field changedInput schema / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …."relate_locations1 field changedInput schema / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …; 30 buildings fields (Overture Maps buildings, release 2026-08-19.0, summed per area): buildings (buildings), multi_story_buildings (multi-story buildings), mid_rise_buildings (mid-rise buildings), high_rise_buildings (high-rise buildings), buildings_2_floors_plus (buildings with 2 published floors and up), buildings_4_floors_plus (buildings with 4 published floors and up), …; 13 building-types fields (Overture Maps buildings, release 2026-08-19.0, summed per area): residential_buildings (residential buildings), outbuilding_buildings (outbuilding buildings), commercial_buildings (commercial buildings), industrial_buildings (industrial buildings), education_buildings (education buildings), agricultural_buildings (agricultural buildings), …; 43 building-classes fields (Overture Maps buildings, release 2026-08-19.0, summed per area): house_class_buildings (house class buildings), detached_class_buildings (detached class buildings), residential_class_buildings (residential class buildings), garage_class_buildings (garage class buildings), apartments_class_buildings (apartments class buildings), shed_class_buildings (shed class buildings), …; 42 building-classes-2 fields (Overture Maps buildings, release 2026-08-19.0, summed per area): religious_class_buildings (religious class buildings), civic_class_buildings (civic class buildings), fire_station_class_buildings (fire station class buildings), bungalow_class_buildings (bungalow class buildings), pavilion_class_buildings (pavilion class buildings), hut_class_buildings (hut class buildings), …; 27 building-roofs fields (Overture Maps buildings, release 2026-08-19.0, summed per area): gabled_roof_buildings (buildings with a gabled roof), flat_roof_buildings (buildings with a flat roof), hipped_roof_buildings (buildings with a hipped roof), mansard_roof_buildings (buildings with a mansard roof), round_roof_buildings (buildings with a round roof), pyramidal_roof_buildings (buildings with a pyramidal roof), …; 11 building-facades fields (Overture Maps buildings, release 2026-08-19.0, summed per area): brick_facade_buildings (buildings with a brick facade), wood_facade_buildings (buildings with a wood facade), metal_facade_buildings (buildings with a metal facade), concrete_facade_buildings (buildings with a concrete facade), plaster_facade_buildings (buildings with a plaster facade), plastic_facade_buildings (buildings with a plastic facade), …."cotenancy1 field changedInput schema / properties / a / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …."count_by_area3 fields changedInput schema / properties / area_where / descriptionPrevious value: -"Only areas meeting a Census condition, each as \"<kind>:<attribute><op><value>\" about the same kind as by (or a state), e.g. \"county:population>500000\" with lacks for \"counties over 500k people with no X\". Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …."New value: +"Only areas meeting a Census condition, each as \"<kind>:<attribute><op><value>\" about the same kind as by (or a state), e.g. \"county:population>500000\" with lacks for \"counties over 500k people with no X\". Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …."Input schema / properties / per_field / descriptionPrevious value: -"With per: the Census count the rate is over instead of population, e.g. per=1000 with per_field=construction_establishments adds per_1000_construction_establishments. Any count attribute: population, households, housing_units, establishments, employees, agriculture_establishments, mining_establishments, utilities_establishments, and every <sector>_establishments / <sector>_employees."New value: +"With per: the Census count the rate is over instead of population, e.g. per=1000 with per_field=construction_establishments adds per_1000_construction_establishments. Any count attribute: population, households, housing_units, establishments, employees, agriculture_establishments, mining_establishments, utilities_establishments, every <sector>_establishments / <sector>_employees, and every naics_<code>_establishments / naics_<code>_employees (2- to 6-digit NAICS, e.g. naics_8111_establishments)."Input schema / properties / sets / items / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …."count_locations1 field changedInput schema / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …."create_query_checkout1 field changedInput schema / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …."email_quote1 field changedInput schema / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …."get_sample1 field changedInput schema / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …."query_locations1 field changedInput schema / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …."relate_locations1 field changedInput schema / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; by detailed NAICS industry (1999 codes, 2 to 6 digits, the Census's titles), naics_<code>_establishments (county/metro/state/zip), naics_<code>_employees and naics_<code>_payroll (county/metro/state), e.g. naics_4471_establishments (gasoline stations naics 4471 establishments), naics_8111_establishments (automotive repair and maintenance establishments), naics_238990_establishments (all other specialty trade contractors naics 238990 establishments), naics_561621_establishments (security systems services except locksmiths establishments); 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …; 51 b01001 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_under_18 (share of residents aged 0 to 17), pct_5_to_14 (share of residents aged 5 to 14), b01001_001 (sex by age total), b01001_002 (sex by age male), b01001_003 (sex by age male 0 to 4 years), b01001_004 (sex by age male 5 to 9 years), …; 20 b11005 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): pct_households_with_people_under_18 (share of households with people aged 0 to 17), b11005_001 (households by presence of people 0 to 17 years by household type total), b11005_002 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years), b11005_003 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households), b11005_004 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households married-couple family), b11005_005 (households by presence of people 0 to 17 years by household type households with people 0 to 17 years family households other family), …; 3 b25003 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b25003_001 (tenure total), b25003_002 (tenure owner occupied), b25003_003 (tenure renter occupied); 13 b08303 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b08303_001 (travel time to work total), b08303_002 (travel time to work 0 to 4 minutes), b08303_003 (travel time to work 5 to 9 minutes), b08303_004 (travel time to work 10 to 14 minutes), b08303_005 (travel time to work 15 to 19 minutes), b08303_006 (travel time to work 20 to 24 minutes), …; 7 b23025 fields (U.S. Census Bureau, ACS 2020–2024 5-year estimates): b23025_001 (employment status for the population 16 years or older total), b23025_002 (employment status for the population 16 years or older in labor force), b23025_003 (employment status for the population 16 years or older in labor force civilian labor force), b23025_004 (employment status for the population 16 years or older in labor force civilian labor force employed), b23025_005 (employment status for the population 16 years or older in labor force civilian labor force unemployed), b23025_006 (employment status for the population 16 years or older in labor force armed forces), …."cotenancy3 fields changedInput schema / properties / a / properties / area_columns / descriptionPrevious value: -"Census facts added to every row as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\". Free."New value: +"Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy. Free. The same attribute words area_where takes (listed there)."Input schema / properties / a / properties / where / items / properties / any_ofAdded value: +{
+ "items": {
+ "additionalProperties": false,
+ "properties": {
+ "field": {
+ "type": "string"
+ },
+ "op": {
+ "enum": [
+ "eq",
+ "ne",
+ "gt",
+ "gte",
+ "lt",
+ "lte",
+ "in",
+ "contains",
+ "starts_with",
+ "is_blank",
+ "not_blank",
+ "not_contains",
+ "not_in",
+ "any_of"
+ ],
+ "type": "string"
+ },
+ "value": {
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "number"
+ },
+ {
+ "type": "boolean"
+ },
+ {
+ "items": {
+ "type": [
+ "string",
+ "number"
+ ]
+ },
+ "type": "array"
+ }
+ ]
+ }
+ },
+ "required": [
+ "field",
+ "op"
+ ],
+ "type": "object"
+ },
+ "maxItems": 12,
+ "type": "array"
+}Input schema / properties / a / properties / where / items / properties / op / enumPrevious value: -[
- "eq",
- "ne",
- "gt",
- "gte",
- "lt",
- "lte",
- "in",
- "contains",
- "starts_with",
- "is_blank",
- "not_blank"
-]New value: +[
+ "eq",
+ "ne",
+ "gt",
+ "gte",
+ "lt",
+ "lte",
+ "in",
+ "contains",
+ "starts_with",
+ "is_blank",
+ "not_blank",
+ "not_contains",
+ "not_in",
+ "any_of"
+]count_by_area3 fields changedInput schema / properties / sets / items / properties / area_columns / descriptionPrevious value: -"Census facts added to every row as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\". Free."New value: +"Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy. Free. The same attribute words area_where takes (listed there)."Input schema / properties / sets / items / properties / where / items / properties / any_ofAdded value: +{
+ "items": {
+ "additionalProperties": false,
+ "properties": {
+ "field": {
+ "type": "string"
+ },
+ "op": {
+ "enum": [
+ "eq",
+ "ne",
+ "gt",
+ "gte",
+ "lt",
+ "lte",
+ "in",
+ "contains",
+ "starts_with",
+ "is_blank",
+ "not_blank",
+ "not_contains",
+ "not_in",
+ "any_of"
+ ],
+ "type": "string"
+ },
+ "value": {
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "number"
+ },
+ {
+ "type": "boolean"
+ },
+ {
+ "items": {
+ "type": [
+ "string",
+ "number"
+ ]
+ },
+ "type": "array"
+ }
+ ]
+ }
+ },
+ "required": [
+ "field",
+ "op"
+ ],
+ "type": "object"
+ },
+ "maxItems": 12,
+ "type": "array"
+}Input schema / properties / sets / items / properties / where / items / properties / op / enumPrevious value: -[
- "eq",
- "ne",
- "gt",
- "gte",
- "lt",
- "lte",
- "in",
- "contains",
- "starts_with",
- "is_blank",
- "not_blank"
-]New value: +[
+ "eq",
+ "ne",
+ "gt",
+ "gte",
+ "lt",
+ "lte",
+ "in",
+ "contains",
+ "starts_with",
+ "is_blank",
+ "not_blank",
+ "not_contains",
+ "not_in",
+ "any_of"
+]count_locations5 fields changedInput schema / properties / area_columns / descriptionPrevious value: -"Census facts added to every row as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\". Free."New value: +"Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy. Free. The same attribute words area_where takes (listed there)."Input schema / properties / metroAdded value: +{
+ "description": "Only rows in one metro area (CBSA): its 5-digit code (\"37980\") or its name (\"Philadelphia\", \"Philadelphia, PA\"). The same as areas_in {\"by\": \"metro\", \"ids\": [code]}: rows are placed the way count_by_area places them, so \"all hospitals in the Philadelphia metro\" is one count, one price and one purchase.",
+ "type": "string"
+}Input schema / properties / where / items / properties / any_ofAdded value: +{
+ "description": "With op any_of: the conditions, any one of which keeps the row (one level).",
+ "items": {
+ "additionalProperties": false,
+ "properties": {
+ "field": {
+ "type": "string"
+ },
+ "op": {
+ "enum": [
+ "eq",
+ "ne",
+ "gt",
+ "gte",
+ "lt",
+ "lte",
+ "in",
+ "contains",
+ "starts_with",
+ "is_blank",
+ "not_blank",
+ "not_contains",
+ "not_in",
+ "any_of"
+ ],
+ "type": "string"
+ },
+ "value": {
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "number"
+ },
+ {
+ "type": "boolean"
+ },
+ {
+ "items": {
+ "type": [
+ "string",
+ "number"
+ ]
+ },
+ "type": "array"
+ }
+ ]
+ }
+ },
+ "required": [
+ "field",
+ "op"
+ ],
+ "type": "object"
+ },
+ "maxItems": 12,
+ "type": "array"
+}Input schema / properties / where / items / properties / op / descriptionPrevious value: -"eq/ne: case-insensitive match (numeric when both sides are numbers; yes/no, true/false, y/n and 1/0 count as the same answer). gt/gte/lt/lte: numeric when value is a number, else text order (works for ISO dates). in: value is an array. contains/starts_with: case-insensitive text. is_blank/not_blank: no value."New value: +"eq/ne: case-insensitive match (numeric when both sides are numbers; yes/no, true/false, y/n and 1/0 count as the same answer). gt/gte/lt/lte: numeric when value is a number, else text order (works for ISO dates). in / not_in: value is an array. contains / not_contains / starts_with: case-insensitive text. is_blank/not_blank: no value (a source's no-value marker such as <UNAVAIL> or N/A counts as blank). any_of: no value; the row is kept when ANY condition in any_of holds (e.g. brand not_in [chains] OR brand is_blank, to keep independents)."Input schema / properties / where / items / properties / op / enumPrevious value: -[
- "eq",
- "ne",
- "gt",
- "gte",
- "lt",
- "lte",
- "in",
- "contains",
- "starts_with",
- "is_blank",
- "not_blank"
-]New value: +[
+ "eq",
+ "ne",
+ "gt",
+ "gte",
+ "lt",
+ "lte",
+ "in",
+ "contains",
+ "starts_with",
+ "is_blank",
+ "not_blank",
+ "not_contains",
+ "not_in",
+ "any_of"
+]create_query_checkout5 fields changedInput schema / properties / area_columns / descriptionPrevious value: -"Census facts added to every row as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\". Free."New value: +"Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy. Free. The same attribute words area_where takes (listed there)."Input schema / properties / metroAdded value: +{
+ "description": "Only rows in one metro area (CBSA): its 5-digit code (\"37980\") or its name (\"Philadelphia\", \"Philadelphia, PA\"). The same as areas_in {\"by\": \"metro\", \"ids\": [code]}: rows are placed the way count_by_area places them, so \"all hospitals in the Philadelphia metro\" is one count, one price and one purchase.",
+ "type": "string"
+}Input schema / properties / where / items / properties / any_ofAdded value: +{
+ "description": "With op any_of: the conditions, any one of which keeps the row (one level).",
+ "items": {
+ "additionalProperties": false,
+ "properties": {
+ "field": {
+ "type": "string"
+ },
+ "op": {
+ "enum": [
+ "eq",
+ "ne",
+ "gt",
+ "gte",
+ "lt",
+ "lte",
+ "in",
+ "contains",
+ "starts_with",
+ "is_blank",
+ "not_blank",
+ "not_contains",
+ "not_in",
+ "any_of"
+ ],
+ "type": "string"
+ },
+ "value": {
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "number"
+ },
+ {
+ "type": "boolean"
+ },
+ {
+ "items": {
+ "type": [
+ "string",
+ "number"
+ ]
+ },
+ "type": "array"
+ }
+ ]
+ }
+ },
+ "required": [
+ "field",
+ "op"
+ ],
+ "type": "object"
+ },
+ "maxItems": 12,
+ "type": "array"
+}Input schema / properties / where / items / properties / op / descriptionPrevious value: -"eq/ne: case-insensitive match (numeric when both sides are numbers; yes/no, true/false, y/n and 1/0 count as the same answer). gt/gte/lt/lte: numeric when value is a number, else text order (works for ISO dates). in: value is an array. contains/starts_with: case-insensitive text. is_blank/not_blank: no value."New value: +"eq/ne: case-insensitive match (numeric when both sides are numbers; yes/no, true/false, y/n and 1/0 count as the same answer). gt/gte/lt/lte: numeric when value is a number, else text order (works for ISO dates). in / not_in: value is an array. contains / not_contains / starts_with: case-insensitive text. is_blank/not_blank: no value (a source's no-value marker such as <UNAVAIL> or N/A counts as blank). any_of: no value; the row is kept when ANY condition in any_of holds (e.g. brand not_in [chains] OR brand is_blank, to keep independents)."Input schema / properties / where / items / properties / op / enumPrevious value: -[
- "eq",
- "ne",
- "gt",
- "gte",
- "lt",
- "lte",
- "in",
- "contains",
- "starts_with",
- "is_blank",
- "not_blank"
-]New value: +[
+ "eq",
+ "ne",
+ "gt",
+ "gte",
+ "lt",
+ "lte",
+ "in",
+ "contains",
+ "starts_with",
+ "is_blank",
+ "not_blank",
+ "not_contains",
+ "not_in",
+ "any_of"
+]email_quote5 fields changedInput schema / properties / area_columns / descriptionPrevious value: -"Census facts added to every row as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\". Free."New value: +"Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy. Free. The same attribute words area_where takes (listed there)."Input schema / properties / metroAdded value: +{
+ "description": "Only rows in one metro area (CBSA): its 5-digit code (\"37980\") or its name (\"Philadelphia\", \"Philadelphia, PA\"). The same as areas_in {\"by\": \"metro\", \"ids\": [code]}: rows are placed the way count_by_area places them, so \"all hospitals in the Philadelphia metro\" is one count, one price and one purchase.",
+ "type": "string"
+}Input schema / properties / where / items / properties / any_ofAdded value: +{
+ "description": "With op any_of: the conditions, any one of which keeps the row (one level).",
+ "items": {
+ "additionalProperties": false,
+ "properties": {
+ "field": {
+ "type": "string"
+ },
+ "op": {
+ "enum": [
+ "eq",
+ "ne",
+ "gt",
+ "gte",
+ "lt",
+ "lte",
+ "in",
+ "contains",
+ "starts_with",
+ "is_blank",
+ "not_blank",
+ "not_contains",
+ "not_in",
+ "any_of"
+ ],
+ "type": "string"
+ },
+ "value": {
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "number"
+ },
+ {
+ "type": "boolean"
+ },
+ {
+ "items": {
+ "type": [
+ "string",
+ "number"
+ ]
+ },
+ "type": "array"
+ }
+ ]
+ }
+ },
+ "required": [
+ "field",
+ "op"
+ ],
+ "type": "object"
+ },
+ "maxItems": 12,
+ "type": "array"
+}Input schema / properties / where / items / properties / op / descriptionPrevious value: -"eq/ne: case-insensitive match (numeric when both sides are numbers; yes/no, true/false, y/n and 1/0 count as the same answer). gt/gte/lt/lte: numeric when value is a number, else text order (works for ISO dates). in: value is an array. contains/starts_with: case-insensitive text. is_blank/not_blank: no value."New value: +"eq/ne: case-insensitive match (numeric when both sides are numbers; yes/no, true/false, y/n and 1/0 count as the same answer). gt/gte/lt/lte: numeric when value is a number, else text order (works for ISO dates). in / not_in: value is an array. contains / not_contains / starts_with: case-insensitive text. is_blank/not_blank: no value (a source's no-value marker such as <UNAVAIL> or N/A counts as blank). any_of: no value; the row is kept when ANY condition in any_of holds (e.g. brand not_in [chains] OR brand is_blank, to keep independents)."Input schema / properties / where / items / properties / op / enumPrevious value: -[
- "eq",
- "ne",
- "gt",
- "gte",
- "lt",
- "lte",
- "in",
- "contains",
- "starts_with",
- "is_blank",
- "not_blank"
-]New value: +[
+ "eq",
+ "ne",
+ "gt",
+ "gte",
+ "lt",
+ "lte",
+ "in",
+ "contains",
+ "starts_with",
+ "is_blank",
+ "not_blank",
+ "not_contains",
+ "not_in",
+ "any_of"
+]get_sample5 fields changedInput schema / properties / area_columns / descriptionPrevious value: -"Census facts added to every row as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\". Free."New value: +"Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy. Free. The same attribute words area_where takes (listed there)."Input schema / properties / metroAdded value: +{
+ "description": "Only rows in one metro area (CBSA): its 5-digit code (\"37980\") or its name (\"Philadelphia\", \"Philadelphia, PA\"). The same as areas_in {\"by\": \"metro\", \"ids\": [code]}: rows are placed the way count_by_area places them, so \"all hospitals in the Philadelphia metro\" is one count, one price and one purchase.",
+ "type": "string"
+}Input schema / properties / where / items / properties / any_ofAdded value: +{
+ "description": "With op any_of: the conditions, any one of which keeps the row (one level).",
+ "items": {
+ "additionalProperties": false,
+ "properties": {
+ "field": {
+ "type": "string"
+ },
+ "op": {
+ "enum": [
+ "eq",
+ "ne",
+ "gt",
+ "gte",
+ "lt",
+ "lte",
+ "in",
+ "contains",
+ "starts_with",
+ "is_blank",
+ "not_blank",
+ "not_contains",
+ "not_in",
+ "any_of"
+ ],
+ "type": "string"
+ },
+ "value": {
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "number"
+ },
+ {
+ "type": "boolean"
+ },
+ {
+ "items": {
+ "type": [
+ "string",
+ "number"
+ ]
+ },
+ "type": "array"
+ }
+ ]
+ }
+ },
+ "required": [
+ "field",
+ "op"
+ ],
+ "type": "object"
+ },
+ "maxItems": 12,
+ "type": "array"
+}Input schema / properties / where / items / properties / op / descriptionPrevious value: -"eq/ne: case-insensitive match (numeric when both sides are numbers; yes/no, true/false, y/n and 1/0 count as the same answer). gt/gte/lt/lte: numeric when value is a number, else text order (works for ISO dates). in: value is an array. contains/starts_with: case-insensitive text. is_blank/not_blank: no value."New value: +"eq/ne: case-insensitive match (numeric when both sides are numbers; yes/no, true/false, y/n and 1/0 count as the same answer). gt/gte/lt/lte: numeric when value is a number, else text order (works for ISO dates). in / not_in: value is an array. contains / not_contains / starts_with: case-insensitive text. is_blank/not_blank: no value (a source's no-value marker such as <UNAVAIL> or N/A counts as blank). any_of: no value; the row is kept when ANY condition in any_of holds (e.g. brand not_in [chains] OR brand is_blank, to keep independents)."Input schema / properties / where / items / properties / op / enumPrevious value: -[
- "eq",
- "ne",
- "gt",
- "gte",
- "lt",
- "lte",
- "in",
- "contains",
- "starts_with",
- "is_blank",
- "not_blank"
-]New value: +[
+ "eq",
+ "ne",
+ "gt",
+ "gte",
+ "lt",
+ "lte",
+ "in",
+ "contains",
+ "starts_with",
+ "is_blank",
+ "not_blank",
+ "not_contains",
+ "not_in",
+ "any_of"
+]query_locations5 fields changedInput schema / properties / area_columns / descriptionPrevious value: -"Census facts added to every row as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\". Free."New value: +"Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy. Free. The same attribute words area_where takes (listed there)."Input schema / properties / metroAdded value: +{
+ "description": "Only rows in one metro area (CBSA): its 5-digit code (\"37980\") or its name (\"Philadelphia\", \"Philadelphia, PA\"). The same as areas_in {\"by\": \"metro\", \"ids\": [code]}: rows are placed the way count_by_area places them, so \"all hospitals in the Philadelphia metro\" is one count, one price and one purchase.",
+ "type": "string"
+}Input schema / properties / where / items / properties / any_ofAdded value: +{
+ "description": "With op any_of: the conditions, any one of which keeps the row (one level).",
+ "items": {
+ "additionalProperties": false,
+ "properties": {
+ "field": {
+ "type": "string"
+ },
+ "op": {
+ "enum": [
+ "eq",
+ "ne",
+ "gt",
+ "gte",
+ "lt",
+ "lte",
+ "in",
+ "contains",
+ "starts_with",
+ "is_blank",
+ "not_blank",
+ "not_contains",
+ "not_in",
+ "any_of"
+ ],
+ "type": "string"
+ },
+ "value": {
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "number"
+ },
+ {
+ "type": "boolean"
+ },
+ {
+ "items": {
+ "type": [
+ "string",
+ "number"
+ ]
+ },
+ "type": "array"
+ }
+ ]
+ }
+ },
+ "required": [
+ "field",
+ "op"
+ ],
+ "type": "object"
+ },
+ "maxItems": 12,
+ "type": "array"
+}Input schema / properties / where / items / properties / op / descriptionPrevious value: -"eq/ne: case-insensitive match (numeric when both sides are numbers; yes/no, true/false, y/n and 1/0 count as the same answer). gt/gte/lt/lte: numeric when value is a number, else text order (works for ISO dates). in: value is an array. contains/starts_with: case-insensitive text. is_blank/not_blank: no value."New value: +"eq/ne: case-insensitive match (numeric when both sides are numbers; yes/no, true/false, y/n and 1/0 count as the same answer). gt/gte/lt/lte: numeric when value is a number, else text order (works for ISO dates). in / not_in: value is an array. contains / not_contains / starts_with: case-insensitive text. is_blank/not_blank: no value (a source's no-value marker such as <UNAVAIL> or N/A counts as blank). any_of: no value; the row is kept when ANY condition in any_of holds (e.g. brand not_in [chains] OR brand is_blank, to keep independents)."Input schema / properties / where / items / properties / op / enumPrevious value: -[
- "eq",
- "ne",
- "gt",
- "gte",
- "lt",
- "lte",
- "in",
- "contains",
- "starts_with",
- "is_blank",
- "not_blank"
-]New value: +[
+ "eq",
+ "ne",
+ "gt",
+ "gte",
+ "lt",
+ "lte",
+ "in",
+ "contains",
+ "starts_with",
+ "is_blank",
+ "not_blank",
+ "not_contains",
+ "not_in",
+ "any_of"
+]relate_locations3 fields changedInput schema / properties / area_columns / descriptionPrevious value: -"Census facts added to every row as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\". Free."New value: +"Census and NOAA facts added to EVERY ROW as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\" — the county / ZIP / metro / state figures beside each location, in the preview and in the file you buy. Free. The same attribute words area_where takes (listed there)."Input schema / properties / where / items / properties / any_ofAdded value: +{
+ "items": {
+ "additionalProperties": false,
+ "properties": {
+ "field": {
+ "type": "string"
+ },
+ "op": {
+ "enum": [
+ "eq",
+ "ne",
+ "gt",
+ "gte",
+ "lt",
+ "lte",
+ "in",
+ "contains",
+ "starts_with",
+ "is_blank",
+ "not_blank",
+ "not_contains",
+ "not_in",
+ "any_of"
+ ],
+ "type": "string"
+ },
+ "value": {
+ "anyOf": [
+ {
+ "type": "string"
+ },
+ {
+ "type": "number"
+ },
+ {
+ "type": "boolean"
+ },
+ {
+ "items": {
+ "type": [
+ "string",
+ "number"
+ ]
+ },
+ "type": "array"
+ }
+ ]
+ }
+ },
+ "required": [
+ "field",
+ "op"
+ ],
+ "type": "object"
+ },
+ "maxItems": 12,
+ "type": "array"
+}Input schema / properties / where / items / properties / op / enumPrevious value: -[
- "eq",
- "ne",
- "gt",
- "gte",
- "lt",
- "lte",
- "in",
- "contains",
- "starts_with",
- "is_blank",
- "not_blank"
-]New value: +[
+ "eq",
+ "ne",
+ "gt",
+ "gte",
+ "lt",
+ "lte",
+ "in",
+ "contains",
+ "starts_with",
+ "is_blank",
+ "not_blank",
+ "not_contains",
+ "not_in",
+ "any_of"
+]count_locations3 fields changedInput schema / properties / limitAdded value: +{
+ "description": "Price a slice instead of every match: the first `limit` rows past `offset` in the delivery order — e.g. near a place with limit 10 for the 10 nearest — exactly the rows a purchase with the same limit returns. The answer's `slice` has their exact price, each row source at its own rate (open-data rows vs rows of lists we sell), and the card price.",
+ "maximum": 10000,
+ "minimum": 1,
+ "type": "integer"
+}Input schema / properties / offsetAdded value: +{
+ "description": "With limit: skip this many matching rows, as a paged purchase does, so the quote is for that page",
+ "maximum": 10000,
+ "minimum": 0,
+ "type": "integer"
+}Input schema / properties / order_byAdded value: +{
+ "additionalProperties": false,
+ "description": "With limit: the order the slice is taken in, as a purchase takes it. Without it, nearest first with `near`, else file order.",
+ "properties": {
+ "direction": {
+ "description": "Default desc",
+ "enum": [
+ "asc",
+ "desc"
+ ],
+ "type": "string"
+ },
+ "field": {
+ "type": "string"
+ }
+ },
+ "required": [
+ "field"
+ ],
+ "type": "object"
+}count_by_area1 field changedInput schema / properties / with_sampleAdded value: +{
+ "description": "Free. On the first 10 areas listed, each set's fixed published sample rows that fall inside the area (source_dataset, the list's columns, matches_your_question). The same published rows every question sees, so cells never add up to the file.",
+ "type": "boolean"
+}create_query_checkout1 field changedInput schema / properties / shapeAdded value: +{
+ "description": "CRM-ready columns: hubspot (Company name, Company domain name, Website URL, Phone number, Street address, Street address 2, City, State/Region, Postal code, Country/Region, Industry, Description) or salesforce (Name, Website, Phone, BillingStreet, BillingCity, BillingState, BillingPostalCode, BillingCountry, Industry, Description) first, then every other column of the list under its own name. Nothing is dropped; a CRM column the list lacks is present and blank.",
+ "enum": [
+ "hubspot",
+ "salesforce"
+ ],
+ "type": "string"
+}get_sample1 field changedInput schema / properties / shapeAdded value: +{
+ "description": "CRM-ready columns: hubspot (Company name, Company domain name, Website URL, Phone number, Street address, Street address 2, City, State/Region, Postal code, Country/Region, Industry, Description) or salesforce (Name, Website, Phone, BillingStreet, BillingCity, BillingState, BillingPostalCode, BillingCountry, Industry, Description) first, then every other column of the list under its own name. Nothing is dropped; a CRM column the list lacks is present and blank.",
+ "enum": [
+ "hubspot",
+ "salesforce"
+ ],
+ "type": "string"
+}query_locations1 field changedInput schema / properties / shapeAdded value: +{
+ "description": "CRM-ready columns: hubspot (Company name, Company domain name, Website URL, Phone number, Street address, Street address 2, City, State/Region, Postal code, Country/Region, Industry, Description) or salesforce (Name, Website, Phone, BillingStreet, BillingCity, BillingState, BillingPostalCode, BillingCountry, Industry, Description) first, then every other column of the list under its own name. Nothing is dropped; a CRM column the list lacks is present and blank.",
+ "enum": [
+ "hubspot",
+ "salesforce"
+ ],
+ "type": "string"
+}cotenancy6 fields changedInput schema / properties / a / properties / near / descriptionAdded value: +"Distance search on lists with coordinates: ONE of place, zip, lat+lng or points, with radius_miles or drive_minutes (not both)."Input schema / properties / a / properties / near / properties / drive_minutesAdded value: +{
+ "description": "Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free).",
+ "maximum": 240,
+ "minimum": 5,
+ "type": "integer"
+}Input schema / properties / a / properties / near / properties / place / descriptionAdded value: +"City or town with state, e.g. \"Topeka, KS\""Input schema / properties / a / properties / near / properties / pointsAdded value: +{
+ "description": "Several points instead of one place/zip/lat+lng (at most 10): a row counts when it is within the radius or drive-time band of ANY of them, e.g. an operator's offices.",
+ "items": {
+ "additionalProperties": false,
+ "properties": {
+ "lat": {
+ "$ref": "#/properties/a/properties/near/properties/lat"
+ },
+ "lng": {
+ "$ref": "#/properties/a/properties/near/properties/lng"
+ },
+ "place": {
+ "$ref": "#/properties/a/properties/near/properties/place"
+ },
+ "zip": {
+ "$ref": "#/properties/a/properties/near/properties/zip"
+ }
+ },
+ "type": "object"
+ },
+ "maxItems": 10,
+ "minItems": 1,
+ "type": "array"
+}Input schema / properties / a / properties / near / properties / radius_miles / descriptionAdded value: +"Only rows within this straight-line distance of the point (or of any of the points). Free."Input schema / properties / a / properties / near / properties / zip / descriptionAdded value: +"5-digit zip"count_by_area6 fields changedInput schema / properties / sets / items / properties / near / descriptionAdded value: +"Distance search on lists with coordinates: ONE of place, zip, lat+lng or points, with radius_miles or drive_minutes (not both)."Input schema / properties / sets / items / properties / near / properties / drive_minutesAdded value: +{
+ "description": "Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free).",
+ "maximum": 240,
+ "minimum": 5,
+ "type": "integer"
+}Input schema / properties / sets / items / properties / near / properties / place / descriptionAdded value: +"City or town with state, e.g. \"Topeka, KS\""Input schema / properties / sets / items / properties / near / properties / pointsAdded value: +{
+ "description": "Several points instead of one place/zip/lat+lng (at most 10): a row counts when it is within the radius or drive-time band of ANY of them, e.g. an operator's offices.",
+ "items": {
+ "additionalProperties": false,
+ "properties": {
+ "lat": {
+ "$ref": "#/properties/sets/items/properties/near/properties/lat"
+ },
+ "lng": {
+ "$ref": "#/properties/sets/items/properties/near/properties/lng"
+ },
+ "place": {
+ "$ref": "#/properties/sets/items/properties/near/properties/place"
+ },
+ "zip": {
+ "$ref": "#/properties/sets/items/properties/near/properties/zip"
+ }
+ },
+ "type": "object"
+ },
+ "maxItems": 10,
+ "minItems": 1,
+ "type": "array"
+}Input schema / properties / sets / items / properties / near / properties / radius_miles / descriptionAdded value: +"Only rows within this straight-line distance of the point (or of any of the points). Free."Input schema / properties / sets / items / properties / near / properties / zip / descriptionAdded value: +"5-digit zip"count_locations6 fields changedInput schema / properties / near / descriptionPrevious value: -"Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, or lat+lng. Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted."New value: +"Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, lat+lng, or `points` (several of those: within reach of ANY of them, distance to the nearest). Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes; 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted."Input schema / properties / near / properties / drive_minutes / descriptionPrevious value: -"Instead of radius_miles: only rows within this many minutes' drive (typical road speeds, no live traffic)"New value: +"Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free)."Input schema / properties / near / properties / drive_minutes / maximumPrevious value: -60New value: +240Input schema / properties / near / properties / pointsAdded value: +{
+ "description": "Several points instead of one place/zip/lat+lng (at most 10): a row counts when it is within the radius or drive-time band of ANY of them, e.g. an operator's offices.",
+ "items": {
+ "additionalProperties": false,
+ "properties": {
+ "lat": {
+ "$ref": "#/properties/near/properties/lat"
+ },
+ "lng": {
+ "$ref": "#/properties/near/properties/lng"
+ },
+ "place": {
+ "description": "City or town with state, e.g. \"Topeka, KS\"",
+ "type": "string"
+ },
+ "zip": {
+ "$ref": "#/properties/near/properties/zip"
+ }
+ },
+ "type": "object"
+ },
+ "maxItems": 10,
+ "minItems": 1,
+ "type": "array"
+}Input schema / properties / near / properties / radius_miles / descriptionPrevious value: -"Only rows within this straight-line distance"New value: +"Only rows within this straight-line distance of the point (or of any of the points). Free."Input schema / properties / near / properties / zip / descriptionPrevious value: -"5-digit zip, e.g. \"66603\""New value: +"5-digit zip"create_query_checkout6 fields changedInput schema / properties / near / descriptionPrevious value: -"Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, or lat+lng. Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted."New value: +"Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, lat+lng, or `points` (several of those: within reach of ANY of them, distance to the nearest). Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes; 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted."Input schema / properties / near / properties / drive_minutes / descriptionPrevious value: -"Instead of radius_miles: only rows within this many minutes' drive (typical road speeds, no live traffic)"New value: +"Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free)."Input schema / properties / near / properties / drive_minutes / maximumPrevious value: -60New value: +240Input schema / properties / near / properties / pointsAdded value: +{
+ "description": "Several points instead of one place/zip/lat+lng (at most 10): a row counts when it is within the radius or drive-time band of ANY of them, e.g. an operator's offices.",
+ "items": {
+ "additionalProperties": false,
+ "properties": {
+ "lat": {
+ "$ref": "#/properties/near/properties/lat"
+ },
+ "lng": {
+ "$ref": "#/properties/near/properties/lng"
+ },
+ "place": {
+ "description": "City or town with state, e.g. \"Topeka, KS\"",
+ "type": "string"
+ },
+ "zip": {
+ "$ref": "#/properties/near/properties/zip"
+ }
+ },
+ "type": "object"
+ },
+ "maxItems": 10,
+ "minItems": 1,
+ "type": "array"
+}Input schema / properties / near / properties / radius_miles / descriptionPrevious value: -"Only rows within this straight-line distance"New value: +"Only rows within this straight-line distance of the point (or of any of the points). Free."Input schema / properties / near / properties / zip / descriptionPrevious value: -"5-digit zip, e.g. \"66603\""New value: +"5-digit zip"email_quote6 fields changedInput schema / properties / near / descriptionPrevious value: -"Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, or lat+lng. Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted."New value: +"Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, lat+lng, or `points` (several of those: within reach of ANY of them, distance to the nearest). Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes; 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted."Input schema / properties / near / properties / drive_minutes / descriptionPrevious value: -"Instead of radius_miles: only rows within this many minutes' drive (typical road speeds, no live traffic)"New value: +"Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free)."Input schema / properties / near / properties / drive_minutes / maximumPrevious value: -60New value: +240Input schema / properties / near / properties / pointsAdded value: +{
+ "description": "Several points instead of one place/zip/lat+lng (at most 10): a row counts when it is within the radius or drive-time band of ANY of them, e.g. an operator's offices.",
+ "items": {
+ "additionalProperties": false,
+ "properties": {
+ "lat": {
+ "$ref": "#/properties/near/properties/lat"
+ },
+ "lng": {
+ "$ref": "#/properties/near/properties/lng"
+ },
+ "place": {
+ "description": "City or town with state, e.g. \"Topeka, KS\"",
+ "type": "string"
+ },
+ "zip": {
+ "$ref": "#/properties/near/properties/zip"
+ }
+ },
+ "type": "object"
+ },
+ "maxItems": 10,
+ "minItems": 1,
+ "type": "array"
+}Input schema / properties / near / properties / radius_miles / descriptionPrevious value: -"Only rows within this straight-line distance"New value: +"Only rows within this straight-line distance of the point (or of any of the points). Free."Input schema / properties / near / properties / zip / descriptionPrevious value: -"5-digit zip, e.g. \"66603\""New value: +"5-digit zip"get_sample6 fields changedInput schema / properties / near / descriptionPrevious value: -"Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, or lat+lng. Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted."New value: +"Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, lat+lng, or `points` (several of those: within reach of ANY of them, distance to the nearest). Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes; 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted."Input schema / properties / near / properties / drive_minutes / descriptionPrevious value: -"Instead of radius_miles: only rows within this many minutes' drive (typical road speeds, no live traffic)"New value: +"Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free)."Input schema / properties / near / properties / drive_minutes / maximumPrevious value: -60New value: +240Input schema / properties / near / properties / pointsAdded value: +{
+ "description": "Several points instead of one place/zip/lat+lng (at most 10): a row counts when it is within the radius or drive-time band of ANY of them, e.g. an operator's offices.",
+ "items": {
+ "additionalProperties": false,
+ "properties": {
+ "lat": {
+ "$ref": "#/properties/near/properties/lat"
+ },
+ "lng": {
+ "$ref": "#/properties/near/properties/lng"
+ },
+ "place": {
+ "description": "City or town with state, e.g. \"Topeka, KS\"",
+ "type": "string"
+ },
+ "zip": {
+ "$ref": "#/properties/near/properties/zip"
+ }
+ },
+ "type": "object"
+ },
+ "maxItems": 10,
+ "minItems": 1,
+ "type": "array"
+}Input schema / properties / near / properties / radius_miles / descriptionPrevious value: -"Only rows within this straight-line distance"New value: +"Only rows within this straight-line distance of the point (or of any of the points). Free."Input schema / properties / near / properties / zip / descriptionPrevious value: -"5-digit zip, e.g. \"66603\""New value: +"5-digit zip"query_locations6 fields changedInput schema / properties / near / descriptionPrevious value: -"Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, or lat+lng. Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted."New value: +"Distance search, on lists with map coordinates (distanceSearch in search results): give ONE of place, zip, lat+lng, or `points` (several of those: within reach of ANY of them, distance to the nearest). Rows come back nearest first with a distance_miles column (straight line); combine with limit for 'the 10 closest', radius_miles for 'everything within 25 miles', or drive_minutes for 'everything within a 30-minute drive' (adds within_drive_minutes; 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles). Give radius_miles or drive_minutes, not both. Rows without coordinates are excluded and counted."Input schema / properties / near / properties / drive_minutes / descriptionPrevious value: -"Instead of radius_miles: only rows within this many minutes' drive (typical road speeds, no live traffic)"New value: +"Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free)."Input schema / properties / near / properties / drive_minutes / maximumPrevious value: -60New value: +240Input schema / properties / near / properties / pointsAdded value: +{
+ "description": "Several points instead of one place/zip/lat+lng (at most 10): a row counts when it is within the radius or drive-time band of ANY of them, e.g. an operator's offices.",
+ "items": {
+ "additionalProperties": false,
+ "properties": {
+ "lat": {
+ "$ref": "#/properties/near/properties/lat"
+ },
+ "lng": {
+ "$ref": "#/properties/near/properties/lng"
+ },
+ "place": {
+ "description": "City or town with state, e.g. \"Topeka, KS\"",
+ "type": "string"
+ },
+ "zip": {
+ "$ref": "#/properties/near/properties/zip"
+ }
+ },
+ "type": "object"
+ },
+ "maxItems": 10,
+ "minItems": 1,
+ "type": "array"
+}Input schema / properties / near / properties / radius_miles / descriptionPrevious value: -"Only rows within this straight-line distance"New value: +"Only rows within this straight-line distance of the point (or of any of the points). Free."Input schema / properties / near / properties / zip / descriptionPrevious value: -"5-digit zip, e.g. \"66603\""New value: +"5-digit zip"relate_locations6 fields changedInput schema / properties / near / descriptionAdded value: +"Distance search on lists with coordinates: ONE of place, zip, lat+lng or points, with radius_miles or drive_minutes (not both)."Input schema / properties / near / properties / drive_minutesAdded value: +{
+ "description": "Instead of radius_miles: only rows a car can reach from the point (or from any of the points) in this many minutes, typical road speeds, no live traffic. On this server: 5-60 minutes; the routing provider draws no longer band, so a 2- or 4-hour reach is not available here — use the largest band, 60 minutes, or radius_miles. One routing call per point per request; on /find it needs an issued key (radius is free).",
+ "maximum": 240,
+ "minimum": 5,
+ "type": "integer"
+}Input schema / properties / near / properties / place / descriptionAdded value: +"City or town with state, e.g. \"Topeka, KS\""Input schema / properties / near / properties / pointsAdded value: +{
+ "description": "Several points instead of one place/zip/lat+lng (at most 10): a row counts when it is within the radius or drive-time band of ANY of them, e.g. an operator's offices.",
+ "items": {
+ "additionalProperties": false,
+ "properties": {
+ "lat": {
+ "$ref": "#/properties/near/properties/lat"
+ },
+ "lng": {
+ "$ref": "#/properties/near/properties/lng"
+ },
+ "place": {
+ "$ref": "#/properties/near/properties/place"
+ },
+ "zip": {
+ "$ref": "#/properties/near/properties/zip"
+ }
+ },
+ "type": "object"
+ },
+ "maxItems": 10,
+ "minItems": 1,
+ "type": "array"
+}Input schema / properties / near / properties / radius_miles / descriptionAdded value: +"Only rows within this straight-line distance of the point (or of any of the points). Free."Input schema / properties / near / properties / zip / descriptionAdded value: +"5-digit zip"count_by_area5 fields changedInput schema / properties / area_columnsAdded value: +{
+ "description": "Census and NOAA figures RETURNED on every area row (in attributes), each as \"<kind>:<attribute>,<attribute>\" about the same kind as by (or \"state:…\" for the area's state, returned as state_<attribute>), e.g. [\"county:population,median_household_income,households\"]. Free. The vocabulary is the area manifest — the same words area_where takes (population, households, median household income, median age, growth since 2020, home values, <sector>_establishments / <sector>_employees by NAICS sector, and the NOAA weather elements); https://locationlists.com/find?areas=county&dataset=<slug>&area_columns=county:<attribute> refuses an unknown attribute by name and lists the nearest ones. Rank the areas by one with order_by.",
+ "items": {
+ "type": "string"
+ },
+ "maxItems": 40,
+ "type": "array"
+}Input schema / properties / limit / descriptionPrevious value: -"Areas listed (default: every matching area when 500 or fewer match, else the first 100)"New value: +"Areas listed per call (default: every matching area when 500 or fewer match, else the first 100; at most 1000)"Input schema / properties / offsetAdded value: +{
+ "description": "Areas to skip before the first listed: page through every matching area with limit + offset. The answer's page block gives total and the next offset (null on the last page).",
+ "maximum": 100000,
+ "minimum": 0,
+ "type": "integer"
+}Input schema / properties / order_byAdded value: +{
+ "additionalProperties": false,
+ "description": "Rank the areas: {\"field\": \"median_household_income\"} for the richest counties first, {\"field\": \"a\", \"direction\": \"asc\"} for the fewest of set a first. Unknown figures sort last.",
+ "properties": {
+ "direction": {
+ "description": "Default desc (name: asc)",
+ "enum": [
+ "asc",
+ "desc"
+ ],
+ "type": "string"
+ },
+ "field": {
+ "description": "count (default), a set label, per (with per), name, or an attribute the rows carry from area_where / per_field / area_columns",
+ "type": "string"
+ }
+ },
+ "required": [
+ "field"
+ ],
+ "type": "object"
+}Input schema / properties / top_valuesAdded value: +{
+ "additionalProperties": false,
+ "description": "The most common values of one class column per area, per set (top on each area row): what kinds of place make up a count — {\"column\": \"category\"} shows that Elkhart County's 47 trailer dealers are 30 dealers, 12 manufacturers, 5 horse-trailer specialists. Free, bounded, and the same figures a filtered count gives. A name, address or contact column is refused: a free answer names classes, never records — the rows are in the file you buy.",
+ "properties": {
+ "column": {
+ "description": "A CLASS column of the sets' files: category, type, brand, tier, status… (get_dataset lists columns)",
+ "type": "string"
+ },
+ "n": {
+ "description": "Values per area, 1-5 (default 3)",
+ "maximum": 5,
+ "minimum": 1,
+ "type": "integer"
+ }
+ },
+ "required": [
+ "column"
+ ],
+ "type": "object"
+}count_locations1 field changedInput schema / properties / areas_inAdded value: +{
+ "additionalProperties": false,
+ "description": "Only rows in these areas, e.g. {\"by\": \"county\", \"ids\": [\"18039\"]} for Elkhart County, IN — the ids count_by_area returns. Rows are placed by county_fips, county + state, coordinates or ZIP, the same as count_by_area.",
+ "properties": {
+ "by": {
+ "enum": [
+ "county",
+ "zip",
+ "state",
+ "metro"
+ ],
+ "type": "string"
+ },
+ "ids": {
+ "description": "County FIPS, 5-digit ZIP, CBSA code or state code — the `id` of a count_by_area row",
+ "items": {
+ "type": "string"
+ },
+ "maxItems": 200,
+ "minItems": 1,
+ "type": "array"
+ }
+ },
+ "required": [
+ "by",
+ "ids"
+ ],
+ "type": "object"
+}create_query_checkout1 field changedInput schema / properties / areas_inAdded value: +{
+ "additionalProperties": false,
+ "description": "Only rows in these areas, e.g. {\"by\": \"county\", \"ids\": [\"18039\"]} for Elkhart County, IN — the ids count_by_area returns. Rows are placed by county_fips, county + state, coordinates or ZIP, the same as count_by_area.",
+ "properties": {
+ "by": {
+ "enum": [
+ "county",
+ "zip",
+ "state",
+ "metro"
+ ],
+ "type": "string"
+ },
+ "ids": {
+ "description": "County FIPS, 5-digit ZIP, CBSA code or state code — the `id` of a count_by_area row",
+ "items": {
+ "type": "string"
+ },
+ "maxItems": 200,
+ "minItems": 1,
+ "type": "array"
+ }
+ },
+ "required": [
+ "by",
+ "ids"
+ ],
+ "type": "object"
+}email_quote1 field changedInput schema / properties / areas_inAdded value: +{
+ "additionalProperties": false,
+ "description": "Only rows in these areas, e.g. {\"by\": \"county\", \"ids\": [\"18039\"]} for Elkhart County, IN — the ids count_by_area returns. Rows are placed by county_fips, county + state, coordinates or ZIP, the same as count_by_area.",
+ "properties": {
+ "by": {
+ "enum": [
+ "county",
+ "zip",
+ "state",
+ "metro"
+ ],
+ "type": "string"
+ },
+ "ids": {
+ "description": "County FIPS, 5-digit ZIP, CBSA code or state code — the `id` of a count_by_area row",
+ "items": {
+ "type": "string"
+ },
+ "maxItems": 200,
+ "minItems": 1,
+ "type": "array"
+ }
+ },
+ "required": [
+ "by",
+ "ids"
+ ],
+ "type": "object"
+}get_sample2 fields changedInput schema / properties / areas_inAdded value: +{
+ "additionalProperties": false,
+ "description": "Only rows in these areas, e.g. {\"by\": \"county\", \"ids\": [\"18039\"]} for Elkhart County, IN — the ids count_by_area returns. Rows are placed by county_fips, county + state, coordinates or ZIP, the same as count_by_area.",
+ "properties": {
+ "by": {
+ "enum": [
+ "county",
+ "zip",
+ "state",
+ "metro"
+ ],
+ "type": "string"
+ },
+ "ids": {
+ "description": "County FIPS, 5-digit ZIP, CBSA code or state code — the `id` of a count_by_area row",
+ "items": {
+ "type": "string"
+ },
+ "maxItems": 200,
+ "minItems": 1,
+ "type": "array"
+ }
+ },
+ "required": [
+ "by",
+ "ids"
+ ],
+ "type": "object"
+}Input schema / properties / rows / descriptionPrevious value: -"Rows to return (default 10; with filters at most 3)"New value: +"Rows to return (default 10; with filters default 3, up to the published sample's size — samplePublished in the answer)"query_locations1 field changedInput schema / properties / areas_inAdded value: +{
+ "additionalProperties": false,
+ "description": "Only rows in these areas, e.g. {\"by\": \"county\", \"ids\": [\"18039\"]} for Elkhart County, IN — the ids count_by_area returns. Rows are placed by county_fips, county + state, coordinates or ZIP, the same as count_by_area.",
+ "properties": {
+ "by": {
+ "enum": [
+ "county",
+ "zip",
+ "state",
+ "metro"
+ ],
+ "type": "string"
+ },
+ "ids": {
+ "description": "County FIPS, 5-digit ZIP, CBSA code or state code — the `id` of a count_by_area row",
+ "items": {
+ "type": "string"
+ },
+ "maxItems": 200,
+ "minItems": 1,
+ "type": "array"
+ }
+ },
+ "required": [
+ "by",
+ "ids"
+ ],
+ "type": "object"
+}relate_locations3 fields changedInput schema / properties / relate / descriptionPrevious value: -"Relate each base row to an anchor set. nearest: the k nearest anchors with miles. count_within: rank by anchors within radius_miles. within_any: rows with an anchor within radius_miles. none_within: rows with none. same_place / not_same_place: base rows that are (or are not) the same physical place as a row of each set — same address, or within 60 m sharing a name word — with every region of the overlap counted; N-way with anchors, also and not_also. next_best: no anchor; needs the base set's state — the candidate sites in that state ranked by how well they match what the base list's own locations typically have nearby, blended with an estimated market capture. Straight-line miles, or real drive time with within_drive_minutes (5-60) on count_within / within_any / none_within, capped at 60 base rows."New value: +"Relate each base row to an anchor set. nearest: the k nearest anchors with miles. count_within: rank by anchors within radius_miles. within_any: rows with an anchor within radius_miles. none_within: rows with none. same_place / not_same_place: base rows that are (or are not) the same physical place as a row of each set — same address, or within 60 m sharing a name word — with every region of the overlap counted; N-way with anchors, also and not_also. next_best: no anchor; needs the base set's state — the candidate sites in that state ranked by how well they match what the base list's own locations typically have nearby, blended with an estimated market capture. Straight-line miles, or real drive time with within_drive_minutes (5-60) on count_within / within_any / none_within, capped at 60 base rows. overlap: a TERRITORY question, not a row count — buffer every base row and every anchor row by radius_miles (straight-line, default 3, 1-25), union each into one shape, and answer what share of the base's shape the anchor's shape covers, plus each base row's own share (lowest first finds the whitespace rows with no nearby anchor territory). Free: the shares, the two territories in square miles and the headline percentage. Paid: the base rows with their own share, at that list's per-row rate."Input schema / properties / relate / properties / mode / descriptionPrevious value: -"nearest | count_within | within_any | none_within | same_place | not_same_place | next_best"New value: +"nearest | count_within | within_any | none_within | same_place | not_same_place | next_best | overlap"Input schema / properties / relate / properties / mode / enumPrevious value: -[
- "nearest",
- "count_within",
- "within_any",
- "none_within",
- "same_place",
- "not_same_place",
- "next_best"
-]New value: +[
+ "nearest",
+ "count_within",
+ "within_any",
+ "none_within",
+ "same_place",
+ "not_same_place",
+ "next_best",
+ "overlap"
+]relate_locations1 field changedInput schema / properties / relate / properties / anchor_allAdded value: +{
+ "description": "count_within / within_any: ONE OF EACH instead of one of any. The anchor set's dataset slugs grouped as the question named them — [[\"a\"],[\"b\"]] keeps only base rows with a row of BOTH within the distance. Omit for the ordinary any-of reading.",
+ "items": {
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+ },
+ "maxItems": 3,
+ "minItems": 2,
+ "type": "array"
+}relate_locations11 fields changedInput schema / properties / relate / descriptionPrevious value: -"Relate each base row to an anchor set. nearest: the k nearest anchors with miles. count_within: rank by anchors within radius_miles. within_any: rows with an anchor within radius_miles. none_within: rows with none. Straight-line miles, or real drive time with within_drive_minutes (5-60) on count_within / within_any / none_within, capped at 60 base rows."New value: +"Relate each base row to an anchor set. nearest: the k nearest anchors with miles. count_within: rank by anchors within radius_miles. within_any: rows with an anchor within radius_miles. none_within: rows with none. same_place / not_same_place: base rows that are (or are not) the same physical place as a row of each set — same address, or within 60 m sharing a name word — with every region of the overlap counted; N-way with anchors, also and not_also. next_best: no anchor; needs the base set's state — the candidate sites in that state ranked by how well they match what the base list's own locations typically have nearby, blended with an estimated market capture. Straight-line miles, or real drive time with within_drive_minutes (5-60) on count_within / within_any / none_within, capped at 60 base rows."Input schema / properties / relate / properties / alsoAdded value: +{
+ "description": "same_place only: the sets a base row must be the same place as some row of, by label (\"b\", \"c\"); default every set",
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+}Input schema / properties / relate / properties / anchor / descriptionPrevious value: -"The other set: dataset, datasets or category, plus filters"New value: +"The other set: dataset, datasets or category, plus filters. Every mode but next_best needs it"Input schema / properties / relate / properties / anchorsAdded value: +{
+ "description": "same_place only: further sets (c, d) after anchor (b), each given the same way",
+ "items": {
+ "additionalProperties": false,
+ "properties": {
+ "area_columns": {
+ "$ref": "#/properties/area_columns"
+ },
+ "area_where": {
+ "$ref": "#/properties/area_where"
+ },
+ "category": {
+ "$ref": "#/properties/category"
+ },
+ "city": {
+ "$ref": "#/properties/city"
+ },
+ "county": {
+ "$ref": "#/properties/county"
+ },
+ "dataset": {
+ "$ref": "#/properties/dataset"
+ },
+ "datasets": {
+ "$ref": "#/properties/datasets"
+ },
+ "exclude": {
+ "$ref": "#/properties/exclude"
+ },
+ "near": {
+ "$ref": "#/properties/near"
+ },
+ "state": {
+ "$ref": "#/properties/state"
+ },
+ "where": {
+ "$ref": "#/properties/where"
+ },
+ "zip": {
+ "$ref": "#/properties/zip"
+ }
+ },
+ "type": "object"
+ },
+ "maxItems": 2,
+ "type": "array"
+}Input schema / properties / relate / properties / k / descriptionAdded value: +"nearest: how many anchors per base row. next_best: H3 rings counted as nearby, 1-3 (default 2, about a mile)"Input schema / properties / relate / properties / lambdaAdded value: +{
+ "description": "next_best only: the Huff distance-decay exponent (default 2, the traditional value)",
+ "exclusiveMinimum": 0,
+ "maximum": 10,
+ "type": "number"
+}Input schema / properties / relate / properties / limitAdded value: +{
+ "description": "next_best only: candidates ranked (default 50)",
+ "maximum": 50,
+ "minimum": 1,
+ "type": "integer"
+}Input schema / properties / relate / properties / mode / descriptionPrevious value: -"nearest | count_within | within_any | none_within"New value: +"nearest | count_within | within_any | none_within | same_place | not_same_place | next_best"Input schema / properties / relate / properties / mode / enumPrevious value: -[
- "nearest",
- "count_within",
- "within_any",
- "none_within"
-]New value: +[
+ "nearest",
+ "count_within",
+ "within_any",
+ "none_within",
+ "same_place",
+ "not_same_place",
+ "next_best"
+]Input schema / properties / relate / properties / not_alsoAdded value: +{
+ "description": "same_place only: the sets a base row must NOT be the same place as any row of; not_same_place with one set is [\"b\"]",
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+}Input schema / properties / relate / requiredPrevious value: -[
- "mode",
- "anchor"
-]New value: +[
+ "mode"
+]cotenancy1 field changedInput schema / properties / a / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …."count_by_area2 fields changedInput schema / properties / area_where / descriptionPrevious value: -"Only areas meeting a Census condition, each as \"<kind>:<attribute><op><value>\" about the same kind as by (or a state), e.g. \"county:population>500000\" with lacks for \"counties over 500k people with no X\". Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services."New value: +"Only areas meeting a Census condition, each as \"<kind>:<attribute><op><value>\" about the same kind as by (or a state), e.g. \"county:population>500000\" with lacks for \"counties over 500k people with no X\". Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …."Input schema / properties / sets / items / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …."count_locations1 field changedInput schema / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …."create_query_checkout1 field changedInput schema / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …."email_quote1 field changedInput schema / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …."get_sample1 field changedInput schema / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …."query_locations1 field changedInput schema / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …."relate_locations1 field changedInput schema / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services; 262 nclimdiv fields (NOAA nClimDiv county climate normals, 1991–2020, and 2025 actuals (release 2026-09-04)): january_high (January high), february_high (February high), march_high (March high), april_high (April high), may_high (May high), june_high (June high), …; 190 storms fields (NOAA Storm Events Database, 2016–2025, events a year by county): astronomical_low_tide_events (astronomical low tide events a year), avalanche_events (avalanche events a year), blizzard_events (blizzard events a year), coastal_flood_events (coastal flood events a year), cold_or_wind_chill_events (cold or wind chill events a year), debris_flow_events (debris flow events a year), …; 127 normals-annual fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): annual_cooling_degree_days_base_40 (annual cooling degree days base 40), annual_cooling_degree_days_base_45 (annual cooling degree days base 45), annual_cooling_degree_days_base_50 (annual cooling degree days base 50), annual_cooling_degree_days_base_55 (annual cooling degree days base 55), annual_cooling_degree_days_base_57 (annual cooling degree days base 57), annual_cooling_degree_days_base_60 (annual cooling degree days base 60), …; 67 normals-winter fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): winter_cooling_degree_days_base_40 (winter cooling degree days base 40), winter_cooling_degree_days_base_45 (winter cooling degree days base 45), winter_cooling_degree_days_base_50 (winter cooling degree days base 50), winter_cooling_degree_days_base_55 (winter cooling degree days base 55), winter_cooling_degree_days_base_57 (winter cooling degree days base 57), winter_cooling_degree_days_base_60 (winter cooling degree days base 60), …; 67 normals-spring fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): spring_cooling_degree_days_base_40 (spring cooling degree days base 40), spring_cooling_degree_days_base_45 (spring cooling degree days base 45), spring_cooling_degree_days_base_50 (spring cooling degree days base 50), spring_cooling_degree_days_base_55 (spring cooling degree days base 55), spring_cooling_degree_days_base_57 (spring cooling degree days base 57), spring_cooling_degree_days_base_60 (spring cooling degree days base 60), …; 67 normals-summer fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): summer_cooling_degree_days_base_40 (summer cooling degree days base 40), summer_cooling_degree_days_base_45 (summer cooling degree days base 45), summer_cooling_degree_days_base_50 (summer cooling degree days base 50), summer_cooling_degree_days_base_55 (summer cooling degree days base 55), summer_cooling_degree_days_base_57 (summer cooling degree days base 57), summer_cooling_degree_days_base_60 (summer cooling degree days base 60), …; 67 normals-fall fields (NOAA U.S. Climate Normals 1991–2020, annual and seasonal station normals, by county): fall_cooling_degree_days_base_40 (fall cooling degree days base 40), fall_cooling_degree_days_base_45 (fall cooling degree days base 45), fall_cooling_degree_days_base_50 (fall cooling degree days base 50), fall_cooling_degree_days_base_55 (fall cooling degree days base 55), fall_cooling_degree_days_base_57 (fall cooling degree days base 57), fall_cooling_degree_days_base_60 (fall cooling degree days base 60), …; 15 hourly fields (NOAA U.S. Climate Normals 1991–2020, hourly station normals summarised over the year, by county): hourly_temperature (round the clock temperature), dew_point (dew point), sea_level_pressure (sea level pressure), cooling_degree_hours (cooling degree hours), heating_degree_hours (heating degree hours), clear_sky_share (share of clear hours), …."cotenancy3 fields changedInput schema / properties / a / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only), establishments (business establishments; county/metro/state only), employees (employees; county/metro/state only)."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services."Input schema / properties / a / properties / excludeAdded value: +{
+ "description": "Dataset slugs left out wherever the set expands: a category minus one of its members (a list's competitors are its own category with itself excluded).",
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+}Input schema / properties / b / properties / excludeAdded value: +{
+ "$ref": "#/properties/a/properties/exclude"
+}count_by_area5 fields changedInput schema / properties / area_where / descriptionPrevious value: -"Only areas meeting a Census condition, each as \"<kind>:<attribute><op><value>\" about the same kind as by (or a state), e.g. \"county:population>500000\" with lacks for \"counties over 500k people with no X\". Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only), establishments (business establishments; county/metro/state only), employees (employees; county/metro/state only)."New value: +"Only areas meeting a Census condition, each as \"<kind>:<attribute><op><value>\" about the same kind as by (or a state), e.g. \"county:population>500000\" with lacks for \"counties over 500k people with no X\". Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services."Input schema / properties / per / descriptionPrevious value: -"Counts per this many residents of each area from its Census population (100000 adds per_100k to every area and ranks by it)."New value: +"Counts per this many residents of each area from its Census population (100000 adds per_100k to every area and ranks by it), or per this many of per_field."Input schema / properties / per_fieldAdded value: +{
+ "description": "With per: the Census count the rate is over instead of population, e.g. per=1000 with per_field=construction_establishments adds per_1000_construction_establishments. Any count attribute: population, households, housing_units, establishments, employees, agriculture_establishments, mining_establishments, utilities_establishments, and every <sector>_establishments / <sector>_employees.",
+ "type": "string"
+}Input schema / properties / sets / items / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only), establishments (business establishments; county/metro/state only), employees (employees; county/metro/state only)."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services."Input schema / properties / sets / items / properties / excludeAdded value: +{
+ "description": "Dataset slugs left out wherever the set expands: a category minus one of its members (a list's competitors are its own category with itself excluded).",
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+}count_locations2 fields changedInput schema / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only), establishments (business establishments; county/metro/state only), employees (employees; county/metro/state only)."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services."Input schema / properties / excludeAdded value: +{
+ "description": "With category or datasets: dataset slugs left out (a category minus one of its members, e.g. a list's competitors are its own category with itself excluded)",
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+}create_query_checkout2 fields changedInput schema / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only), establishments (business establishments; county/metro/state only), employees (employees; county/metro/state only)."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services."Input schema / properties / excludeAdded value: +{
+ "description": "With category or datasets: dataset slugs left out (a category minus one of its members, e.g. a list's competitors are its own category with itself excluded)",
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+}email_quote2 fields changedInput schema / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only), establishments (business establishments; county/metro/state only), employees (employees; county/metro/state only)."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services."Input schema / properties / excludeAdded value: +{
+ "description": "With category or datasets: dataset slugs left out (a category minus one of its members, e.g. a list's competitors are its own category with itself excluded)",
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+}get_sample2 fields changedInput schema / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only), establishments (business establishments; county/metro/state only), employees (employees; county/metro/state only)."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services."Input schema / properties / excludeAdded value: +{
+ "description": "With category or datasets: dataset slugs left out (a category minus one of its members, e.g. a list's competitors are its own category with itself excluded)",
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+}query_locations2 fields changedInput schema / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only), establishments (business establishments; county/metro/state only), employees (employees; county/metro/state only)."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services."Input schema / properties / excludeAdded value: +{
+ "description": "With category or datasets: dataset slugs left out (a category minus one of its members, e.g. a list's competitors are its own category with itself excluded)",
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+}relate_locations3 fields changedInput schema / properties / area_where / descriptionPrevious value: -"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only), establishments (business establishments; county/metro/state only), employees (employees; county/metro/state only)."New value: +"Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), establishments (business establishments), employees (employees), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only); by NAICS sector, <sector>_establishments and <sector>_employees (employees: county/metro/state only) for agriculture, mining, utilities, construction, manufacturing, wholesale_trade, retail_trade, transportation, information, finance, real_estate, professional, management, administrative, educational_services, health_care, arts, accommodation, other_services."Input schema / properties / excludeAdded value: +{
+ "description": "Dataset slugs left out wherever the set expands: a category minus one of its members (a list's competitors are its own category with itself excluded).",
+ "items": {
+ "type": "string"
+ },
+ "type": "array"
+}Input schema / properties / relate / properties / anchor / properties / excludeAdded value: +{
+ "$ref": "#/properties/exclude"
+}cotenancy4 fields changedInput schema / properties / a / properties / area_columnsAdded value: +{
+ "description": "Census facts added to every row as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\". Free.",
+ "items": {
+ "type": "string"
+ },
+ "maxItems": 6,
+ "type": "array"
+}Input schema / properties / a / properties / area_whereAdded value: +{
+ "description": "Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only), establishments (business establishments; county/metro/state only), employees (employees; county/metro/state only).",
+ "items": {
+ "type": "string"
+ },
+ "maxItems": 6,
+ "type": "array"
+}Input schema / properties / b / properties / area_columnsAdded value: +{
+ "$ref": "#/properties/a/properties/area_columns"
+}Input schema / properties / b / properties / area_whereAdded value: +{
+ "$ref": "#/properties/a/properties/area_where"
+}count_by_area4 fields changedInput schema / properties / area_whereAdded value: +{
+ "description": "Only areas meeting a Census condition, each as \"<kind>:<attribute><op><value>\" about the same kind as by (or a state), e.g. \"county:population>500000\" with lacks for \"counties over 500k people with no X\". Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only), establishments (business establishments; county/metro/state only), employees (employees; county/metro/state only).",
+ "items": {
+ "type": "string"
+ },
+ "maxItems": 6,
+ "type": "array"
+}Input schema / properties / perAdded value: +{
+ "description": "Counts per this many residents of each area from its Census population (100000 adds per_100k to every area and ranks by it).",
+ "minimum": 1,
+ "type": "number"
+}Input schema / properties / sets / items / properties / area_columnsAdded value: +{
+ "description": "Census facts added to every row as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\". Free.",
+ "items": {
+ "type": "string"
+ },
+ "maxItems": 6,
+ "type": "array"
+}Input schema / properties / sets / items / properties / area_whereAdded value: +{
+ "description": "Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only), establishments (business establishments; county/metro/state only), employees (employees; county/metro/state only).",
+ "items": {
+ "type": "string"
+ },
+ "maxItems": 6,
+ "type": "array"
+}count_locations2 fields changedInput schema / properties / area_columnsAdded value: +{
+ "description": "Census facts added to every row as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\". Free.",
+ "items": {
+ "type": "string"
+ },
+ "maxItems": 6,
+ "type": "array"
+}Input schema / properties / area_whereAdded value: +{
+ "description": "Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only), establishments (business establishments; county/metro/state only), employees (employees; county/metro/state only).",
+ "items": {
+ "type": "string"
+ },
+ "maxItems": 6,
+ "type": "array"
+}create_query_checkout2 fields changedInput schema / properties / area_columnsAdded value: +{
+ "description": "Census facts added to every row as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\". Free.",
+ "items": {
+ "type": "string"
+ },
+ "maxItems": 6,
+ "type": "array"
+}Input schema / properties / area_whereAdded value: +{
+ "description": "Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only), establishments (business establishments; county/metro/state only), employees (employees; county/metro/state only).",
+ "items": {
+ "type": "string"
+ },
+ "maxItems": 6,
+ "type": "array"
+}email_quote2 fields changedInput schema / properties / area_columnsAdded value: +{
+ "description": "Census facts added to every row as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\". Free.",
+ "items": {
+ "type": "string"
+ },
+ "maxItems": 6,
+ "type": "array"
+}Input schema / properties / area_whereAdded value: +{
+ "description": "Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only), establishments (business establishments; county/metro/state only), employees (employees; county/metro/state only).",
+ "items": {
+ "type": "string"
+ },
+ "maxItems": 6,
+ "type": "array"
+}get_sample2 fields changedInput schema / properties / area_columnsAdded value: +{
+ "description": "Census facts added to every row as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\". Free.",
+ "items": {
+ "type": "string"
+ },
+ "maxItems": 6,
+ "type": "array"
+}Input schema / properties / area_whereAdded value: +{
+ "description": "Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only), establishments (business establishments; county/metro/state only), employees (employees; county/metro/state only).",
+ "items": {
+ "type": "string"
+ },
+ "maxItems": 6,
+ "type": "array"
+}query_locations2 fields changedInput schema / properties / area_columnsAdded value: +{
+ "description": "Census facts added to every row as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\". Free.",
+ "items": {
+ "type": "string"
+ },
+ "maxItems": 6,
+ "type": "array"
+}Input schema / properties / area_whereAdded value: +{
+ "description": "Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only), establishments (business establishments; county/metro/state only), employees (employees; county/metro/state only).",
+ "items": {
+ "type": "string"
+ },
+ "maxItems": 6,
+ "type": "array"
+}relate_locations4 fields changedInput schema / properties / area_columnsAdded value: +{
+ "description": "Census facts added to every row as columns named <kind>_<attribute>, each as \"<kind>:<attribute>,<attribute>\", e.g. \"county:population,median_household_income\". Free.",
+ "items": {
+ "type": "string"
+ },
+ "maxItems": 6,
+ "type": "array"
+}Input schema / properties / area_whereAdded value: +{
+ "description": "Only rows whose county/zip/state/metro meets a Census condition, each as \"<kind>:<attribute><op><value>\" (ops > >= < <= =; values accept 1M, 250k, $50,000, 10%), e.g. \"county:population>1000000\". Every clause must hold; the fact is the area's, not the row's, and it adds nothing to the price. Attributes: population (population), households (households), median_household_income (median household income), median_age (median age), pct_65_plus (share of residents aged 65 or older), pct_bachelors_plus (share with a bachelor's degree or higher), housing_units (housing units), owner_occupied_share (owner-occupied share), median_home_value (median home value), population_estimate (population estimate; county/metro/state only), population_growth_since_2020 (population growth since 2020; county/metro/state only), establishments (business establishments; county/metro/state only), employees (employees; county/metro/state only).",
+ "items": {
+ "type": "string"
+ },
+ "maxItems": 6,
+ "type": "array"
+}Input schema / properties / relate / properties / anchor / properties / area_columnsAdded value: +{
+ "$ref": "#/properties/area_columns"
+}Input schema / properties / relate / properties / anchor / properties / area_whereAdded value: +{
+ "$ref": "#/properties/area_where"
+}Counts and firmographics for 7.5M+ verified US B2B businesses, by industry, state and city.
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