Open Census MCP Server

Understanding and Using American Community Survey Data What State and Local Government Users Need to Know Issued August 2020 Acknowledgments Linda A. Jacobsen, Vice President, U.S. Programs, Population Reference Bureau (PRB), and Mark Mather, Associate Vice President, U.S. Programs, PRB, drafted this handbook in partnership with the U.S. Census Bureau’s American Community Survey Office. Other PRB staff who assisted in drafting and reviewing the handbook include Lillian Kilduff and Paola Scommegna. Some of the material in this handbook was adapted from the Census Bureau’s 2009 publication, A Compass for Understanding and Using American Community Survey Data: What State and Local Governments Need to Know, drafted by Joseph J. Salvo, Arun Peter Lobo, and Joel A. Alvarez. American Community Survey data users who provided feedback and case studies for this handbook include: Susan Brower, Joel Alvarez, Joseph Salvo, John Wilson, Chandler Felt, Susan Kinne, and Gregg Bell. Nicole Scanniello, Gretchen Gooding, and Amanda Klimek, Census Bureau, contributed to the planning and review of this handbook series. The American Community Survey program is under the direction of Albert E. Fontenot, Jr., Associate Director for Decennial Census Programs, Deborah M. Stempowski, Acting Assistant Director for Decennial Census Programs, and Donna M. Daily, Chief, American Community Survey Office. Other individuals from the Census Bureau who contributed to the review and release of these handbooks include: Justin Keller, Jason Lizarraga, Michael Starsinic, R. Chase Sawyer, and Janice Valdisera. Faye Brock, Linda Chen, and Christine Geter provided publication management, graphic design and composition, and editorial review for print and electronic media under the direction of Janet Sweeney, Chief of the Graphic and Editorial Services Branch, Public Information Office. Understanding and Using American Community Survey Data What State and Local Government Users Need to Know Issued August 2020 U.S. Department of Commerce Wilbur Ross, Secretary Karen Dunn Kelley, Deputy Secretary U.S. CENSUS BUREAU Steven Dillingham, Director Suggested Citation U.S. Census Bureau, Understanding and Using American Community Survey Data: What State and Local Government Users Need to Know, U.S. Government Printing Office, Washington, DC, 2020. U.S. CENSUS BUREAU Steven Dillingham, Director Ron Jarmin, Deputy Director and Chief Operating Officer Albert E. Fontenot, Jr., Associate Director for Decennial Census Programs Deborah M. Stempowski, Acting Assistant Director for Decennial Census Programs Donna M. Daily, Chief, American Community Survey Office Contents 1. How State and Local Governments Use ACS Data . . . . . . . . . . . . . . . . . . . . . . .2 2. Considerations When Working With ACS Data . . . . . . . . . . . . . . . . . . . . . . . . . .4 3. Case Studies Using ACS Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6 4. Additional Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Understanding and Using American Community Survey Data iii What State and Local Government Users Need to Know iii U.S. Census Bureau This page is intentionally blank. UNDERSTANDING AND USING AMERICAN COMMUNITY SURVEY DATA: WHAT STATE AND LOCAL GOVERNMENT USERS NEED TO KNOW A primary mission of state and local governments is to deliver efficient and effective services and enact poli- cies that advance public safety and economic growth. For more than a decade, the U.S. Census Bureau’s American Community Survey (ACS) has provided data to help governments meet the needs of their con- stituents. The ACS provides a dynamic picture of the population and housing attributes of states, counties, and municipalities, large and small. The ACS has an annual sample size of about 3.5 million addresses, with survey information collected nearly every day of the year. Data are pooled across a calen- dar year to produce estimates for that year. As a result, ACS estimates reflect data that have been collected over a period of time rather than for a single point in time as in the decennial census, which is conducted every 10 years and provides population counts as of April 1 of the census year. This guide provides a brief overview of how state and local governments are using ACS data to develop general plans, to help implement and evaluate local ini- tiatives, to attract and retain businesses, and for other applications. It also describes some important con- siderations when working with ACS data—especially estimates for small geographic areas. What Is the ACS? The ACS is a nationwide survey designed to provide communities with reliable and timely social, economic, housing, and demographic data every year. A sepa- rate annual survey, called the Puerto Rico Community Survey (PRCS), collects similar data about the popu- lation and housing units in Puerto Rico. The Census Bureau uses data collected in the ACS and the PRCS to provide estimates on a broad range of population, housing unit, and household characteristics for states, counties, cities, school districts, congressional districts, census tracts, block groups, and many other geo- graphic areas. ACS 1-year estimates are data that have been col- lected over a 12-month period and are available for geographic areas with at least 65,000 people. Starting with the 2014 ACS, the Census Bureau is also produc- ing “1-year Supplemental Estimates”—simplified ver- sions of popular ACS tables—for geographic areas with at least 20,000 people. The Census Bureau combines 5 consecutive years of ACS data to produce multiyear estimates for geographic areas with fewer than 65,000 residents. These 5-year estimates represent data col- lected over a period of 60 months. For more detailed information about the ACS—how to judge the accuracy of ACS estimates, understand- ing multiyear estimates, knowing which geographic areas are covered in the ACS, and how to access ACS data on the Census Bureau’s Web site—see the Census Bureau’s handbook on Understanding and Using American Community Survey Data: What All Data Users Need to Know.1 1 U.S. Census Bureau, Understanding and Using American Community Survey Data: What All Data Users Need to Know, <www.census.gov/programs-surveys/acs/guidance/handbooks /general.html>. Understanding and Using American Community Survey Data 1 What State and Local Government Users Need to Know 1 U.S. Census Bureau1. HOW STATE AND LOCAL GOVERNMENTS USE ACS DATA Using ACS Data for Planning Establishing Priorities Through a Needs Assessment Given competing demands and limited resources at their disposal, governments need to carefully ascer- tain appropriate funding levels for their initiatives. Governments also receive requests for help from community groups and civic organizations that must be evaluated and prioritized for funding. American Community Survey (ACS) data can be extremely useful in evaluating the overall needs of the community and identifying subgroups most in need of various services in order to prioritize requests for assistance. • The town of Wenham, Massachusetts, used ACS data (e.g., household type and size, disability status, poverty, income, race, age, employment, units in structure, housing value, and rent costs) to help document priority housing needs and develop strategies to address them.2 • The District of Columbia incorporated ACS data (e.g., median family income, per capita income, marital status, unemployment, and means of travel to work) in their Community Health Needs Assessment, which was designed to identify key trends in health and well-being to inform “public health policies, programs, and interventions to strengthen community health.”3 Developing and Implementing a General Plan Once a government decides on its priorities, it needs to examine various alternative courses of action to come up with an effective plan. If, for example, a local government decides to make the alleviation of poverty a priority, it needs to examine where exactly to apply its resources. Should the alleviation of child poverty be a priority, or should the focus be on poverty among older adults? Or should resources be applied in some proportion to each of these groups? Examination of ACS data could be instrumental in formulating plans and actions to guide the distribution of resources. For example, the California city of Milpitas relied on ACS data to help develop their draft Consolidated Plan 2 Town of Wenham, Housing Needs Assessment, 2017, <www.wenhamma.gov/docs/Wenham%20Housing%20Needs %20Assessment%202017%204-3.pdf>. 3 District of Columbia Community Health Needs Assessment, 2014, <https://dchealth.dc.gov/page/dc-community-health-needs -assessment>. for 2017 to 2022. Their draft plan proposes various strategies to meet the needs of community members, including maintaining and preserving existing housing, supporting public services for lower-income families and individuals, and improving access to public facili- ties.4 Once a plan is chosen, it must be implemented. If, for example, a local government decides to focus primar- ily on increasing resources for low-income older adults, ACS data could be used to target neighborhoods with the largest concentrations of older individuals in need of services. Special Considerations: Environmental Justice and Social Equity Analysis While state and local governments have a variety of resources they can use to ensure that plans and projects meet environmental justice and social equity goals, the ACS can play an important role. Sometimes the impact area for a project is fairly small. Census tract or block group data from the ACS can be used to identify the populations impacted by the project. • The Minnesota Department of Commerce used tract-level ACS estimates of minority and low- income populations to help decision-makers iden- tify the potential impact of a new pipeline project on vulnerable populations.5 • ACS data can also be used to measure English- speaking proficiency and languages spoken in a project area. Both measures can help determine whether a project is in compliance with Title VI of the Civil Rights Act of 1964, which prohibits dis- criminatory practices in programs receiving federal funds. Using ACS Data for Program and Project Evaluation While the ACS was not designed specifically for program evaluation, the comprehensive and timely nature of the data can make it a valuable resource for government analysts who want to assess conditions before and after a policy or plan change, or before and 4 City of Milpitas, Draft Consolidated Plan, 2017–2022, 2017, <www.ci.milpitas.ca.gov/wp-content/uploads/2017/05/Draft -Con-Plan_website.pdf>. 5 Minnesota Department of Commerce, “Energy Environmental Review and Analysis: Final Environmental Impact Statement, Line 3 Project,” Chapter 11: Environmental Justice, 2017, <https://mn.gov /eera/web/project-file?legacyPath=/opt/documents/34079/Line3 %20FEIS%20Ch%2011%20Environmental%20Justice%20Complete .pdf>. 2 Understanding and Using American Community Survey Data 2 What State and Local Government Users Need to Know U.S. Census Bureauafter the implementation of a project. For example, a city may use ACS commuting data to track trends in bicycle commutes to work before and after expanding a network of local bikeways. • Economic vulnerability (e.g., number of workers, industry sectors, earnings, and poverty). • Social vulnerability (e.g., age, disability status, lan- guage proficiency, and vehicle access). TIP: Since ACS data are collected using the same meth- ods across the United States, those who are evaluat- ing programs can compare outcomes in communities where a policy change has occurred with communities that have similar characteristics but have not imple- mented the policy change. Using ACS Data for Economic Development Many businesses use ACS data to gauge the sales potential of products and services, better understand the workforce, and set strategies for growth. However, state and local governments can also influence eco- nomic development through policies to attract or retain businesses. State agencies, chambers of com- merce, and other associations of businesses, like the Greater Houston Partnership, use ACS data to profile the economic, demographic, and workforce charac- teristics of their state’s regions, counties, and cities to attract new businesses. TIP: Because ACS data include comparable data for cities and counties nationwide, they provide a useful benchmark for businesses making decisions about site selection or strategies for growth. Hawaii’s Department of Business, Economic Development & Tourism uses ACS data to compare economic indicators for Hawaii—including unemploy- ment, the old-age dependency ratio, women’s share of the labor force, and income—with economic indicators for other states.6 Using ACS Data for Emergency Management In addition to policy, planning, and economic develop- ment roles, state and local governments have impor- tant responsibilities in disaster response and emer- gency management. Data from the ACS can provide useful context for first responders and for disaster recovery personnel. For example, data from the ACS can help identify: • Physical vulnerability (e.g., vacant and occupied housing units, mobile homes, and the year housing structures were built). These pieces of information can assist local officials as they coordinate evacuations, conduct damage assess- ments, and carry out recovery plans. For example, the Northern Virginia Regional Commission created a dashboard on population groups that may be vulnerable to the coronavirus, based on the U.S. Centers for Disease Control and Prevention’s 2018 Social Vulnerability Index. The index was developed using 2014–2018 ACS 5-year estimates.7 Using ACS Data for Local and Regional Forecasts and Modeling State and local government leaders often work across jurisdictional boundaries through metropolitan and regional planning commissions. ACS data are vital for these commissions to help identify and address issues related to housing, transportation, land use, environ- mental protection, and economic development. • The Delaware Valley Regional Planning Commission uses ACS data, in combination with their own transportation survey data, to pro- duce travel simulation models for the greater Philadelphia region.8 • Many metropolitan and regional planners also use data from the Census Transportation Planning Products, or CTPP, which provide a wealth of small-area estimates based on ACS 5-year data for transportation analysis and planning.9 The CTPP program is designed to help transportation ana- lysts and planners understand where people are commuting to and from and how they get there. The information is organized by where workers live, where they work, and by the flow between those places. 7 Northern Virginia Regional Commission, Northern Virginia Coronavirus Cases and Vulnerable Populations - Impact Planning Report, <https://nvrc.maps.arcgis.com/apps/opsdashboard/index .html#/d47407a16ebb46b5aec7df60af368a5f>. 8 Delaware Valley Regional Planning Commission, Data Sources, <www.dvrpc.org/transportation/modeling/data/>. 6 Hawaii.gov, Department of Business, Economic Development & Tourism, Hawaii Rankings and Comparisons, <http://dbedt.hawaii.gov /economic/ranks/>. 9 American Association of State Highway and Transportation Officials, Census Transportation Planning Products Program, <https://ctpp.transportation.org/>. Understanding and Using American Community Survey Data 3 What State and Local Government Users Need to Know 3 U.S. Census Bureau2. CONSIDERATIONS WHEN WORKING WITH ACS DATA Considerations When Working With ACS Data The greatest strength of the American Community Survey (ACS) is that it provides access to estimates on an annual basis, but this also results in an array of options that affect how data can be used effectively by state and local governments. Most local governments represent relatively small geo- graphic areas that must rely on ACS 5-year estimates. Of the approximately 69,000 states, counties, cities, towns, townships, villages, other minor civil divisions, and census designated places, more than 90 percent rely on 5-year estimates exclusively. This is because most local governments are small, serving geographic areas with fewer than 20,000 people. Among counties and county equivalents, 41 percent rely on 5-year estimates exclusively, while 26 per- cent meet the 65,000-population threshold needed to receive 1-year estimates.10 Data users interested in ACS estimates for areas with populations of 65,000 or more have a choice between the 1-year and 5-year data series. Which data should be used? The 1-year estimates for an area reflect the most current data but they have larger margins of error (MOEs)—indicating less reliability or precision—than the 5-year estimates because they are based on a smaller sample. The 5-year estimates for an area have larger samples and smaller MOEs than the 1-year estimates. However, they are less current because the larger samples include data that were collected in ear- lier years. The main advantage of using multiyear esti- mates is the increased statistical reliability for smaller geographic areas and small population groups. TIP: In the end, what makes the most sense is a mat- ter of judgment regarding the balance between the period covered by an estimate and its level of reliability or precision. The key is to strive to use only reliable estimates, where the period covered best suits the question at hand. Many state and local government data users focus on small geographic areas such as census tracts and block groups. Even with 5 years of pooled data, ACS estimates for these small areas often have large MOEs. TIP: State and local data users need to use good judg- ment by paying attention to measures of reliability— such as MOEs—that indicate whether ACS data are useful “straight out of the box,” or whether some type of data aggregation (e.g., combining geographic areas or data categories) is required to increase reliability. For example, New York City’s Department of City Planning aggregates census tracts into “Neighborhood Tabulation Areas” to increase data reliability. ACS data are then provided for these neighborhoods—rather than individual census tracts—to support local govern- ment decision-making.11 Finally, there is the issue of how to use multiyear characterizations of an area to measure change over time. As the ACS program has moved forward, a whole series of multiyear estimates for various time intervals has become available. TIP: Data users now have access to nonoverlapping ACS 5-year estimates that have increased the value and utility of the data for monitoring trends in local communities. However, it is more challenging to capture rapid change in areas where only ACS 5-year estimates are available. For example, it was very difficult for local officials and planners to accurately assess changes in socioeconomic characteristics accompanying expanded drilling in the Bakken oil fields in North Dakota—where there was a large influx of male work- ers starting in the early 2000s—because the affected counties only received 5-year, rather than 1-year, ACS estimates. For more information about ACS multiyear esti- mates and sampling error, see the sections on “Understanding and Using ACS Single-Year and Multiyear Estimates” and “Understanding Error and Determining Statistical Significance” in the U.S. Census Bureau’s handbook on Understanding and Using American Community Survey Data: What All Data Users Need to Know.12 11 To explore differences in ACS data reliability between census tracts and Neighborhood Tabulation Areas, see NYC Department of City Planning’s ACS data aggregation tool, NYC Population FactFinder, <https://popfactfinder.planning.nyc.gov/>. 10 Percentages include data for municipios in Puerto Rico. For more information, see the Census Bureau’s ACS Web page on Areas Published, available at <www.census.gov/programs-surveys /acs/geography-acs/areas-published.html>. 12 U.S. Census Bureau, Understanding and Using American Community Survey Data: What All Data Users Need to Know, <www.census.gov/programs-surveys/acs/guidance/handbooks /general.html>. 4 Understanding and Using American Community Survey Data 4 What State and Local Government Users Need to Know U.S. Census Bureau Using ACS Data for Population and Housing Counts Many state and local governments need reliable data on the number of people and housing units in their jurisdiction and how those numbers have changed over time. TIP: Such users need to understand that the ACS was designed to provide estimates of the characteristics of the population, not to provide counts of the population in different geographic areas or population subgroups. Therefore, data users are encouraged to rely more upon noncount statistics, such as percent distributions or averages, when using ACS estimates. The Census Bureau’s Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities and towns, and estimates of housing units for states and counties.13 For 2010 and other decennial census years, the decennial census provides the official counts of population and housing units.14 The ACS uses a weighting method to ensure that estimates are consistent with official Census Bureau population estimates at the county level by age, sex, race, and Hispanic origin—as well as estimates of total 13 U.S. Census Bureau, Population and Housing Unit Estimates, <www.census.gov/programs-surveys/popest.html>. 14 See, for example, the U.S. Census Bureau, Census of Population and Housing, CPH-2. Population and Housing Unit Counts report series, <www.census.gov/prod/www/decennial.html>. housing units. ACS 1-year estimates are controlled to population and total housing unit estimates as of July 1 of the survey year, while ACS 5-year estimates are controlled to the average of the July 1 population and housing unit estimates over the 5-year period. Starting with the 2009 survey, ACS estimates of the total population of incorporated places (self-governing cities, towns, or villages) and minor civil divisions (county subdivisions, in 20 states where they serve as functioning governmental units) are also adjusted so that they are consistent with official population esti- mates. However, ACS data for other statistical areas, such as Public Use Microdata Areas (PUMAs) or census tracts, have no control totals, which may lead to larger MOEs of population and housing unit estimates than in areas of similar size with control totals. In such cases, data users are again encouraged to rely more on non- count statistics, such as percentage distributions or averages. For more information about ACS methods, visit the Census Bureau’s Design and Methodology Report Web page.15 15 U.S. Census Bureau, American Community Survey (ACS), Design and Methodology Report, <www.census.gov/programs-surveys/acs /methodology/design-and-methodology.html>. Understanding and Using American Community Survey Data 5 What State and Local Government Users Need to Know 5 U.S. Census Bureau3. CASE STUDIES USING ACS DATA Case Study #1: Minnesota State Demographic Center Analysis of Earnings in Urban and Rural Areas Skill Level: Intermediate/Advanced Subject: Earnings, Rural-Urban Geographic Areas Type of Analysis: Making comparisons across geographic areas Creating custom geographic areas from census tracts Calculating margins of error for derived estimates Tools Used: Data.census.gov, spreadsheet, U.S. Census Bureau’s Statistical Testing Tool Author: Susan Brower, State Demographer of Minnesota Susan is the State Demographer of Minnesota. She wants to study how earnings differ across geographic regions of the state. She plans to use a rural-urban typology that corresponds to the characteristics of individual census tracts. Susan will use Rural-Urban Commuting Area (RUCA) classification codes developed by the U.S. Department of Agriculture’s (USDA) Economic Research Service (ERS) to examine economic characteristics of Minnesota residents living in a range of settings—from remote, rural areas to dense, urban cities. RUCA codes classify census tracts using measures of population density, urbanization, and commuting patterns. Susan will aggregate characteristics of residents across the state, based on the RUCA code of the census tract in which they live. (More information about RUCA codes can be found on the ERS Web page on Rural-Urban Commuting Area Codes.)16 16 U.S. Department of Agriculture, Economic Research Service, “Rural-Urban Commuting Area Codes,” <www.ers.usda.gov/data -products/rural-urban-commuting-area-codes/>. Census tracts are roughly equivalent to neighbor- hoods. They contain 2,500 to 8,000 people per tract. Since detailed American Community Survey (ACS) 1-year estimates are only available for geographic areas with at least 65,000 residents, Susan will use ACS 5-year estimates, which she will download from data.census.gov. There are roughly 1,300 census tracts in Minnesota. Susan will aggregate these tracts into four RUCA- based areas—Rural, Small Town, Large Town, and Urban. She will also estimate how much uncertainty is associated with the new estimates she has created. The U.S. Census Bureau provides a number of formulas that can be used to estimate uncertainty, or margins of error (MOEs), for estimates that are produced from calculations based on published data tables. Calculating the estimates of uncertainty will allow her to make judgments about whether observed differ- ences in earnings are real or whether they are within the expected variations that result from survey sam- pling. 6 Understanding and Using American Community Survey Data 6 What State and Local Government Users Need to Know U.S. Census Bureau Susan starts her analysis by going to the data.census.gov Web site at <https://data.census.gov>. • She clicks on “Advanced Search” under the search bar (see Figure 3.1). Figure 3.1. Selecting Advanced Search in Data.census.gov Source: U.S. Census Bureau, data.census.gov <https://data.census.gov>. Understanding and Using American Community Survey Data 7 What State and Local Government Users Need to Know 7 U.S. Census Bureau• Since Susan already knows her desired table ID, she types “B20005” in the first text box directly under the Advanced Search heading. B20005 is the table ID for “Sex by Work Experience in the Past 12 Months by Earnings in the Past 12 Months (in 2018 Inflation-Adjusted Dollars) for the Population 16 Years and Over.” • She selects “Geography” to view the geography filters. • Next, she selects “Tract,” and scrolls to select “Minnesota” from the “Within (State)” filter. • Susan then checks the box for “All Census Tracts within Minnesota” from the “Within (County)” filter and clicks “Search” in the lower right corner (see Figure 3.2). Figure 3.2. Selecting a Table and Geographies in Data.census.gov Source: U.S. Census Bureau, data.census.gov, <https://data.census.gov>. 8 Understanding and Using American Community Survey Data 8 What State and Local Government Users Need to Know U.S. Census Bureau• On the next page, Susan clicks “Tables” in the upper left corner. • Then, she selects “Download Table” under the message that the “table is too large to display” (see Figure 3.3). Figure 3.3. Downloading a Table From Data.census.gov Source: U.S. Census Bureau, data.census.gov, <https://data.census.gov>. • Next, she uses the checkboxes to select the 2015 ACS 5-year data. • She chooses the File Type “CSV.” • Then, she clicks “Download” in the lower right corner (see Figure 3.4). Figure 3.4. Changing the Survey Year and Choosing a File Type in Data.census.gov Source: U.S. Census Bureau, data.census.gov, <https://data.census.gov>. Understanding and Using American Community Survey Data 9 What State and Local Government Users Need to Know 9 U.S. Census Bureau• Susan selects “Download Now” after the file is prepared (see Figure 3.5). Figure 3.5. Downloading the Compressed (ZIP) File Source: U.S. Census Bureau, data.census.gov, <https://data.census.gov>. • From the compressed folder, Susan opens the file with “data_with_overlays” shown in the file name. Documentation relating to the data table is also included in her zipped file. • Now that Susan has her data file, she analyzes how earnings vary across the rural-urban areas of her state. The USDA publishes 10 primary RUCA codes that delineate census tracts.17 Susan adds these codes to the ACS data file that she has sorted by geographic identifier (GEO_ID). • Using Excel, Susan “copies” two columns of Minnesota census tract data from the USDA RUCA file—the “Primary RUCA Code 2010” and the “State-County-Tract FIPS Code.” (FIPS refers to Federal Information Processing Standards.) She then pastes these two columns of data into the ACS data file. • To verify that the census tract data from the two files are properly matched in the new file, Susan creates a column with the final 11 numbers of the ACS file’s “id” column.18 The last 11 numbers in the “id” column are the tract’s FIPS code. Susan then subtracts this new column (FIPS code) from her ACS file from the “State- County-Tract FIPS Codes” column from her RUCA file. If the rows match, the resulting difference will be zero. • Susan analyzes earnings for a collapsed version of the RUCA codes. She creates a new column of data with four string values: “Urban” for RUCA codes 1–3, “Large Town” for codes 4–6, “Small Town” for codes 7–9, and “Rural” for code 10. 17 U.S. Department of Agriculture, Economic Research Service, “Documentation: 2010 Rural-Urban Commuting Area (RUCA) Codes,” <www.ers.usda.gov/data-products/rural-urban-commuting-area-codes/documentation/>. 18 The Census Bureau has published detailed instructions for matching these GEOIDs: <https://ask.census.gov/prweb/PRServletCustom /YACFBFye-rFIz_FoGtyvDRUGg1Uzu5Mn*/!STANDARD?pyActivity=pyMobileSnapStart&ArticleID=KCP-5651>. 10 Understanding and Using American Community Survey Data 10 What State and Local Government Users Need to Know U.S. Census Bureau• The resulting data file now looks like this, with the highlighted cells added from the USDA RUCA file and Susan’s subsequent recoding and match verification (see Figure 3.6). Figure 3.6. Adding Rural-Urban Codes to Downloaded Data Source: Author’s analysis of data from the U.S. Census Bureau, American Community Survey, 5-Year Estimates; and USDA RUCA codes. • Susan uses “PivotTables” in Excel to aggregate the earnings distribution across census tracts. The PivotTables sums the number of males working full-time, year-round by rural, small town, large town, and urban census tracts within each earnings distribution category (see Figure 3.7). Figure 3.7. Cross-Tabulating Persons Per Income Level and Rural-Urban Category Source: Author’s analysis of data from the U.S. Census Bureau, American Community Survey, 5-Year Estimates; and USDA RUCA codes. • Next, Susan estimates the median earnings of men who work full-time, year-round and live in rural areas. The Census Bureau provides guidance on how to interpolate a median from a weighted distribution in its Accuracy of the PUMS documentation.19 Susan creates an Excel spreadsheet to estimate a median using the method described in the Census Bureau’s documentation. The documentation also describes how to calcu- late standard errors and confidence intervals for her estimates.20 19 U.S. Census Bureau, American Community Survey (ACS), PUMS Technical Documentation, Accuracy of the PUMS, <www.census.gov /programs-surveys/acs/technical-documentation/pums/documentation.html>. 20 The method described in this case study to approximate a median estimate will not match medians published in data.census.gov, as the published medians are calculated using different and more detailed distributions than are available to users. Also, the approximated MOE of the median using this method may underestimate or overestimate the true MOE, due to the limitations of using the PUMS design factor methodology. Understanding and Using American Community Survey Data 11 What State and Local Government Users Need to Know 11 U.S. Census Bureau• She repeats these calculations for men’s and women’s earnings in each of her four geographic areas (see Figure 3.8). Figure 3.8. Calculating a Median From a Weighted Distribution Source: Author’s analysis of data from the U.S. Census Bureau, American Community Survey, 5-Year Estimates; and USDA RUCA codes. • When Susan compiles the calculated medians and their standard errors into a single table, she can see that median earnings for men in urban areas ($55,064) appear to be higher than the median earnings for men in rural, small town, and large town regions of the state. Similarly, urban women’s median earnings ($45,053) are considerably higher than those for women living outside of urban areas. To calculate MOEs for the approximate median earnings, Susan multiplies 1.645 by the standard error of each median. This creates an MOE at the 90 percent confidence level (see Figure 3.9). Figure 3.9. Median Earnings for Men and Women in Minnesota by Rural-Urban Location Median Earnings, Men, Full-Time, Year-Round Workers Minnesota: 2011–2015 Male, rural . . . . . . . . . . . Male, small town . . . . . Male, large town . . . . . . Male, urban . . . . . . . . . . Median earnings $43,838 $44,948 $45,929 $55,064 Standard error Margin of error (90%) $302 $381 $362 $170 $498 $628 $596 $280 Median Earnings, Women, Full-Time, Year-Round Workers Median Standard Margin of Female, rural . . . . . . . . . Female, small town . . . Female, large town . . . Female, urban . . . . . . . . $33,476 $33,070 $34,960 $45,053 $288 $297 $262 $151 $475 $488 $432 $248 Source: Author’s analysis of data from the U.S. Census Bureau, American Community Survey, 5-Year Estimates; and USDA RUCA codes. 12 Understanding and Using American Community Survey Data 12 What State and Local Government Users Need to Know U.S. Census Bureau• Susan then tests whether the calculated differences in median earnings across geographic areas are statistically significant. She pastes the estimated medians and MOEs into the Census Bureau’s Statistical Testing Tool and learns that, as expected, urban men’s median earnings are significantly different from their counterparts in rural areas, small towns, and large towns.21 She also confirms that urban women’s median earnings are statistically different from those of women in other areas of the state (see Figure 3.10). • Figure 3.10. Statistical Testing Tool for Multiple Estimates (90 Percent Confidence Level) Source: U.S. Census Bureau, Statistical Testing Tool, <www.census.gov/programs-surveys/acs/guidance/statistical-testing-tool.html>. Susan uses this analysis to help her convey differences in earnings among residents of rural, small town, large town, and urban areas in reports that her office produces for state policymakers. While she will not always report the numeric results of statistical tests, knowing which differences are significant helps her know which differences she can highlight in her narrative. Conversely, knowing which differences are not statistically significant helps her know which differences she should downplay in her reporting. An example of a report that was informed by this type of analysis is Greater Minnesota: Refined & Revisited.22 (This report was produced using 2010–2014 ACS 5-year estimates, so the medians are somewhat different, but the results are consistent with what is described here.) This report has been used by policymakers working on rural health care initiatives, on Equal Employment Opportunity activities, and by legislators working to create policies that align with current economic conditions in different areas of the state. 21 U.S. Census Bureau, Statistical Testing Tool, <www.census.gov/programs-surveys/acs/guidance/statistical-testing-tool.html>. 22 Minnesota State Demographic Center, Greater Minnesota: Refined & Revisited, <https://mn.gov/admin/demography/reports-resources /greater-mn-refined-and-revisited.jsp>. Understanding and Using American Community Survey Data 13 What State and Local Government Users Need to Know 13 U.S. Census BureauCase Study #2: New York City, Department of City Planning, Uncertainty in Mapping ACS Data Skill Level: Intermediate/Advanced Subject: Uncertainty in Mapping American Community Survey (ACS) Data Type of Analysis: Assessment of statistical reliability of ACS maps Tool Used: Map Reliability Calculator Authors: Joel A. Alvarez, Senior Analyst, NYC City Planning, Population Division; and Joseph J. Salvo, Director, NYC City Planning, Population Division In the summer of 2017, New York City established the New York Works plan—a series of 25 initiatives to pro- mote the creation of 100,000 new jobs with good wages over the next decade.23 In support of the plan, the Department of City Planning (DCP) produced a series of maps informing the public about employment pat- terns in New York City. In this case study, we walk ACS data users through the process we used to assess the reliability of map classification schemes when produc- ing maps for general consumption. Box 3.1. Establishing a Minimum Reliability Threshold for Maps ACS data provide city planners with unique insights into the socioeconomic characteristics of local popu- lations, including information about employment. Mapping the data is one way to examine differences in employment across geographic areas. However, ACS estimates are subject to sampling variability, so reality on the ground may differ from survey results.24 Given the uncertainty associated with ACS estimates, data users should exercise caution when producing maps to avoid misrepresenting the characteristic(s) being dis- played. The following case study provides guidance in this regard, demonstrating how we produced statisti- cally reliable maps of employment and unemployment using an online Map Reliability Calculator.25 Mapping Employment In support of a mayoral jobs creation initiative, DCP was asked to create a series of maps showing the lat- est information on employment and unemployment. One possible approach was to examine administrative data from unemployment insurance filings. However, this data set excludes many self-employed workers and those working “off-the-books,” so we turned to the ACS as a more comprehensive source of data on local employment patterns. First, we examined overall employment in New York City. Our preference was to produce a map using small geographic units, making census tracts ideal. However, in New York City, census tracts typically consist of only six to eight city blocks and have populations of about 3,000 to 4,000. Consequently, ACS 5-year estimates for census tracts are based on small sample sizes—typically 250 to 300 people surveyed in each tract. To ensure that our map was reliable and would Subjects covered in the ACS often display mean- ingful spatial patterning at very fine levels of geography. ACS data users may be tempted to present these data in maps using the smallest available geographic units. However, the reliability of ACS estimates typically decreases as units of analysis get smaller, because of diminishing sam- ple sizes. When mapping ACS data, users must decide whether to use small geographic areas and see all the fine detail, but risk false conclusions due to data uncertainty; or to use large, statisti- cally reliable geographic areas, but risk overlook- ing the most salient spatial distributions. This dilemma can be resolved by establishing a minimum reliability threshold. Once map quality is assured by passing the threshold, ACS data users can pursue mapping at the smallest geographic area for which reliable data are available. New York City’s Department of City Planning (DCP) has adopted a threshold of a 10 percent error rate, under which a map is considered suitable for gen- eral use. A 10 percent error rate means that any given geographic area would have a 1 in 10 chance of being erroneously classed, placing it at odds with reality on the ground. This threshold was adopted because it matches the Census Bureau’s standard of 90 percent confidence intervals. Additionally, the DCP standard is to ensure that no individual map category has an error rate of 20 percent or more, so that map users can trust the reliability of each respective map class. While this is a lower standard than that used for the overall map, it helps ensure that even categories with relatively few values—and therefore little influence on the overall reliability—can still be trusted by end users. 23 City of New York, New York Works, <https://newyorkworks.cityofnewyork.us/introduction/>. 24 Sampling variability is the difference between an estimate based on a sample and the corresponding value that would be obtained if the estimate were based on the entire population. 25 Statistical reliability refers to the ability of a measurement tool to consistently produce the same results. When used in reference to the ACS, the measurement tool is the survey itself. 14 Understanding and Using American Community Survey Data 14 What State and Local Government Users Need to Know U.S. Census Bureaunot mislead people into making false conclusions, we tested the preliminary map using an online Map Reliability Calculator developed by DCP (see Box 3.1 on Establishing a Minimum Reliability Threshold for Maps).26 To conduct this analysis of map reliability, we first went to the U.S. Census Bureau’s data.census.gov located at <https://data.census.gov> and downloaded data on the employed population aged 16 and older in the civil- ian labor force, at the census tract level (from Table B23025).27 The data were then imported into a Geographic Information System (GIS) to produce a map with seven categories using a natural breaks classification scheme.28 We then tested the results using the Map Reliability Calculator. The reliability calculator has three required inputs: • The estimates and associated margins of error (MOEs). • The number of classes or map categories. • The lower limit for each class. After inserting this information into the tool, we examined the results and found that our proposed map was not reliable (see Figure 3.11). When the reliability calculator marks a set of map categories as “not reliable,” it means that 10 percent or more of the geographic areas are potentially misclassified (that is, included in the wrong cat- egory). In our example, shown in Figure 3.11, the overall reliability of the map was 14.2 percent. This means that of New York City’s 2,167 census tracts, more than 300 may have been incorrectly classified. Further, the second- and fourth-highest map classes in our proposed map had reliability scores of more than 20 percent. As with the overall map, reliability scores for individual map classes tell users the percentage of geographic areas that are likely to be misclassified based on the published MOEs. These excessive scores for individual map categories also marked our proposed map as too unreliable for general use. 26 New York City Department of City Planning, Map Reliability Calculator, <https://www1.nyc.gov/site/planning/data-maps/nyc-population /geographic-reference.page>. 27 U.S. Census Bureau, data.census.gov, <https://data.census.gov/>. 28 A Geographic Information System, or GIS, is an application used for mapping, managing, and analyzing spatial data. Various map classifica- tion schemes can be employed when creating categories for quantitative data. We used a natural breaks scheme for our employment analysis. This scheme maximizes the variance between classes, while minimizing variance within classes. Figure 3.11. Results From Map Reliability Calculator (Seven Class Breaks) Note: Estimates with blank margins of error (MOEs) are treated as having MOEs of zero. Source: New York City Department of City Planning, Map Reliability Calculator, <https://www1.nyc.gov/site/planning/data-maps/nyc-population /geographic-reference.page>. Understanding and Using American Community Survey Data 15 What State and Local Government Users Need to Know 15 U.S. Census BureauOne method of improving map reliability is to reduce the number of map classes. Based on this logic, we decreased the number of categories in the proposed map to six, but the map was still not reliable. It wasn’t until the map was reduced to four categories that it qualified as reliable. Further, to make the categories more pre- sentable, we rounded the class breaks and checked to confirm that the map was still reliable (see Figure 3.12). Figure 3.12. Results From Map Reliability Calculator (Four Class Breaks) Note: Estimates with blank margins of error (MOEs) are treated as having MOEs of zero. Source: New York City Department of City Planning, Map Reliability Calculator, <https://www1.nyc.gov/site/planning/data-maps /nyc-population/geographic-reference.page>. 16 Understanding and Using American Community Survey Data 16 What State and Local Government Users Need to Know U.S. Census BureauWith this evaluation, we were confident that our map provided a relatively reliable depiction of reality on the ground and went ahead with its use supporting the mayoral initiative (see Figure 3.13). Figure 3.13. Map of Employed Population Source: Author’s analysis of data from the U.S. Census Bureau, American Community Survey, 5-Year Estimates. Mapping Unemployment Generally, the relative size of ACS MOEs increases in relation to associated ACS estimates as count estimates get smaller. It follows that smaller estimates are often less reliable, in a relative sense.29 Consequently, maps built using smaller ACS estimates are typically less reliable than those built using large estimates. We confronted this issue when we attempted to map unemployment estimates for New York City, since the unemployed population is usually much smaller than the employed population. (The unemployed population is only about one-tenth the size of the employed population in New York City.) Because of the relatively large MOEs, we could only produce a reliable map of census tracts if we sorted them into two categories—one for tracts with 250 or more unemployed persons and one for tracts with fewer than 250 unemployed. While such a map would be informative, we wanted 29 Because estimates and associated MOEs vary greatly in size, it helps to examine the size of MOEs in relation to estimates to better under- stand the relative reliability of ACS estimates. ACS analysts often use Coefficients of Variation (CVs) as a measure of relative reliability—making it possible to compare the reliability of ACS estimates across different years, periods (1-year vs. 5-year periods), geographic areas, and variables. For more information on CVs, see the section on “Understanding Error and Determining Statistical Significance” in Understanding and Using American Community Survey Data: What All Data Users Need to Know, <www.census.gov/programs-surveys/acs/guidance/handbooks/general .html>. Understanding and Using American Community Survey Data 17 What State and Local Government Users Need to Know 17 U.S. Census Bureauto give the public a greater understanding of the differences in unemployment across our city. For this reason, a higher-order geographic area, Neighborhood Tabulation Areas (NTAs), was evaluated for mapping suitability. NTAs were created by DCP using aggregates of census tracts that approximate New York City neighborhoods and fit perfectly within Public Use Microdata Area (PUMA) boundaries. This geographic area has gained wide- spread acceptance and use in New York City because of its relative statistical reliability and because New Yorkers tend to think in terms of neighborhoods. However, since the Census Bureau does not publish data at the NTA level, we needed to calculate new estimates and MOEs aggregating from published, tract-level, unemploy- ment data.30 Using NTAs, a reliable map of unemployment was produced with four categories—as with employ- ment, breakpoints were rounded to make the map more presentable (see Figure 3.14). 30 For more information on calculating MOEs for aggregated count estimates, see the section on “Calculating Measures of Error for Derived Estimates” in Understanding and Using American Community Survey Data: What All Data Users Need to Know, <www.census.gov/programs -surveys/acs/guidance/handbooks/general.html>. Figure 3.14. Map of Unemployed Population Source: Author’s analysis of data from the U.S. Census Bureau, American Community Survey, 5-Year Estimates. 18 Understanding and Using American Community Survey Data 18 What State and Local Government Users Need to Know U.S. Census BureauMapping Change in Employment The 2011–2015 ACS data release provided us with our first opportunity to compare two nonoverlapping 5-year period estimates (2006–2010 and 2011–2015) based on common population controls derived from the 2010 Census and, for the most part, common geographic boundaries. Therefore, we wanted to map change in the employed population as well. To conduct an evaluation of map reliability, it was necessary to first calculate the tract-level changes in employment and calculate the MOEs associated with those changes.31 These calculations were quite simple, because we could use the same formula we used when calculating the MOEs for aggregate areas: the square root of the sum of the squared MOEs.32 Again, it was our preference to create a tract-level map, so we first calculated employment change and associated MOEs for census tracts. Once calculated, estimates and MOEs were inserted into the Map Reliability Calculator. Employment had increased substantially across the city (up nearly 200,000 or 5 percent), so we were surprised to find that a reliable tract map could not be produced, no matter how few categories were used. As with the map of unemployment, we turned to NTAs, a higher-order geographic area, to see if change could be reliably mapped. Change in employment, however, could not pass reliability thresholds using a natural breaks classifica- tion scheme. Therefore, PUMAs, the next higher order statistical geography, were considered. PUMA employ- ment estimates and MOEs from 2006–2010 had to be calculated using census tract aggregations (as with NTAs), because PUMA boundaries changed in 2012, and 2011–2015 estimates were based on the 2012 boundaries. Unfortunately, as with census tracts and NTAs, the PUMA geographic level proved to be unreliable for a natural breaks classification scheme. With no reliable results, we re-examined our calculator analysis for all three geographic areas. Map classification schemes that were close to being reliable were manipulated to test whether they could pass reliability thresholds with a set of alternate breakpoints. We found that we could produce a reliable NTA map by slightly adjusting the breakpoint between the first and second categories of a two-class, natural-breaks map (see Figure 3.15). 31 The Census Bureau endorses the use of statistical testing to gauge the reliability of change over time. This testing tells users that the direc- tionality of change has a 9 in 10 chance of being correct. However, to gauge the reliability of the magnitude of change, it is important that ACS data users go beyond this basic test and consider the MOE associated with the estimate of change. 32 For detailed guidance on “Comparing Estimates for Nonoverlapping Periods” see page 4 in the Census Bureau’s “Instructions for Applying Statistical Testing to the 2011–2015 ACS 5-Year Data,” available at <www.census.gov/programs-surveys/acs/technical-documentation/code -lists.2015.html>. Figure 3.15. Results From Map Reliability Calculator (Two Class Breaks) Source: New York City Department of City Planning, Map Reliability Calculator, <https://www1.nyc.gov/site/planning/data-maps /nyc-population/geographic-reference.page>. Understanding and Using American Community Survey Data 19 What State and Local Government Users Need to Know 19 U.S. Census BureauBecause our lowest map category encompassed both positive and negative change in employment, we chose to only emphasize the top category, where change was equal to, or exceeded, an employment increase of 2,000 (see Figure 3.16). Figure 3.16. Map of Change in Employed Population Source: Author’s analysis of data from the U.S. Census Bureau, American Community Survey, 5-Year Estimates. 20 Understanding and Using American Community Survey Data 20 What State and Local Government Users Need to Know U.S. Census BureauConclusion In producing this series of maps depicting dimensions of employment in New York City, we learned quite a bit about producing reliable maps for general use. In creating a tract-level map of employment, we learned that map reliability can typically be improved by reducing the number of map categories. Additionally, through the production of the unemployment map, we found that map reliability can usually be improved by using higher- order geographic areas, because the reliability of underlying estimates is improved. Finally, while generating a map showing change in employment, we discovered that category breakpoints can be adjusted to make a map statistically reliable. This was an important lesson, because it is ultimately up to each end user to decide which breaks work best for their purposes. While we decided to use a mix of different geographic types in our maps, others might opt for uniformity in their publication summary level. In fact, data users have several different options in mapping ACS data. For example: • Choosing different classification schemes, such as equal interval or quantile schemes. • Selecting fewer map categories to reduce the risk of misclassification. • Normalizing data using percentages (as opposed to using counts). • Loosening map reliability standards to gain insight into a very generalized spatial distribution— acknowledging that such a map is more prone to error.33 Regardless of your approach, it is essential that ACS data mappers pursue their cartographic endeavors with a full understanding that uncertainty is inherent in all survey data, including ACS data, and will impact the quality of maps. It is ultimately up to each end user to decide which standards are most appropriate for their applica- tions. 33 The NYC Department of City Planning’s Map Reliability Calculator provides reliability scores so that users can select alternative thresholds if they choose. Understanding and Using American Community Survey Data 21 What State and Local Government Users Need to Know 21 U.S. Census BureauCase Study #3: King County Housing Assessment Skill Level: Intermediate/Advanced Subject: Evaluating Housing Program Participation Type of Analysis: American Community Survey (ACS) microdata analysis Tools Used: ACS Public Use Microdata Sample File and data.census.gov Authors: John Wilson, Assessor, King County, WA, Department of Assessments; Chandler Felt, Demographer, King County, WA; and Susan Kinne, Epidemiologist at Public Health-Seattle and King County John Wilson: When I became King County (WA) assessor in 2016, housing affordability was headed towards a crisis level—especially for low-income seniors, disabled vet- erans, and other disabled individuals. King County has 2.1 million residents, and real estate values had been rising at a double-digit pace annually. I was curious how many people were enrolled in a state-authorized property tax exemption program. It turned out to be only about 15,000 countywide. That number seemed low to me, so I contacted Chandler Felt, King County’s demographer. I asked Chandler, knowing how familiar he was with U.S. Census Bureau data, if he knew of any way to determine how many people in King County might be eligible for the program. Chandler suggested the lat- est American Community Survey (ACS) data. Chandler Felt: As demographer for the county, I turned to the Census Bureau’s ACS via data.census.gov.34 I looked through the available tables in data.census.gov using the 2014 ACS 1-year data set and the 2010–2014 ACS 5-year data set, but soon realized that the data.cen- sus.gov tables would not provide the entire list of eli- gibility criteria for the exemption. The ACS Public Use Microdata Sample (PUMS) data set would be required to slice our population precisely enough to answer the question, and I do not have experience using the PUMS data.35 I forwarded John’s request to my col- league Susan Kinne, Epidemiologist at Public Health- Seattle and King County, who is a skilled PUMS user. King County’s senior tax exemption is based on three eligibility criteria, all from household data: • Household tenure = owner (as opposed to renter). • Age of householder is 62 or older. • Household income is less than $40,000. 34 Data.census.gov was not available at the time this case study was written but is cited here because it is now the primary tool for ac- cessing ACS data. 35 The ACS PUMS files are a set of untabulated records with infor- mation about individual people or housing units. The Census Bureau produces the PUMS files so that data users can create custom tables that are not available through pretabulated (or summary) ACS data products. Using the regular data.census.gov tables, I could only report and analyze these criteria two at a time—and not very precisely at that. Income by age is available for householders aged 65 and over, and the cross tabulation of owners by age was likewise for 65-year- olds. Generating a series of data.census.gov tables, I developed a rough estimate that up to 34,000 households—4.2 percent of the over 800,000 house- holds in the county—might be eligible as of 2014. Assessor John Wilson and I agreed that a more reli- able estimate was needed, so we asked Susan Kinne to conduct a PUMS analysis, using the three eligibility criteria listed above. For this analysis, Susan used data from the 2010–2014 ACS 5-year PUMS file because it was the most recent data available at the time. The 5-year PUMS files are multiyear combinations of the 1-year PUMS file with appropriate adjustments to the weights and infla- tion adjustment factors. She chose to use the 5-year file because it yields more reliable estimates than the 1-year file, and she was conducting an analysis for a relatively small geographic area and population sub- group (older homeowners living in King County). 22 Understanding and Using American Community Survey Data 22 What State and Local Government Users Need to Know U.S. Census Bureau Here are the steps she took to produce an estimate of the number of homeowners aged 62 and older in King County who may be eligible for a property tax exemption: 1. Using data.census.gov (Table S2501), Susan first found an estimate of the total number of occupied housing units in King County, WA, in 2010–2014 (808,729) (see Figure 3.17). 2. Using statistical software, she read in the data from the 2010–2014 ACS 5-year PUMS file.36 3. Next, she used the PUMS Data Dictionary to find the variables she needed to conduct her analysis.37 4. From her previous work with the PUMS data, she knew that King County was made up of 11 Public Use Microdata Areas, or PUMAs, ranging from PUMA 11606 through PUMA 11616. She selected these PUMAs using the PUMA10 variable in the data set.38 5. Next, she selected the PUMS variables and categories she needed to determine the percentage of occupied housing units in King County headed by homeowners aged 62 and older. a. AGEP (Age) >= 62 b. RELP (Relationship) = 0 (Household reference person) c. TEN (Tenure) = 1 (Owned with a mortgage) or 2 (Owned without a mortgage) 6. A cross-tabulation of these variables showed that approximately 16.2 percent of occupied housing units were headed by homeowners aged 62 and older. Applying that estimate to the total number of occupied housing units from data.census.gov (808,729) yielded an estimate of about 131,000 occupied housing units headed by older homeowners. 7. As a final step, she used the HINCP (Household Income) variable to estimate that among the 131,000 housing units headed by older adults, approximately 40,000 (31 percent) had incomes below the $40,000 tax exemp- tion threshold. 36 U.S. Census Bureau, American Community Survey (ACS), PUMS Data, <www.census.gov/programs-surveys/acs/data/pums.html>. 37 U.S. Census Bureau, American Community Survey (ACS), PUMS Technical Documentation, <www.census.gov/programs-surveys/acs /technical-documentation/pums/documentation.html>. 38 PUMAs are special nonoverlapping areas that partition each state into contiguous geographic units. Each contains roughly 100,000 people. Figure 3.17. Table of Occupancy Characteristics in King County, Washington: 2010–2014 Source: U.S. Census Bureau, data.census.gov, <https://data.census.gov>. Understanding and Using American Community Survey Data 23 What State and Local Government Users Need to Know 23 U.S. Census BureauConclusion The results suggested that there could be 25,000 low-income homeowners eligible to participate in the tax exemption program who were not enrolled (40,000 minus 15,000 currently enrolled). We set into action an outreach plan to increase enrollment. By reaching into certain neighborhoods with large numbers of lower-income homeowners, we were able to increase the number of homeowners applying for the program. After 18 months, the Department of Assessments has brought in nearly 7,500 new applications. That represents a nearly 50 percent increase in enrollment. 24 Understanding and Using American Community Survey Data 24 What State and Local Government Users Need to Know U.S. Census Bureau4. ADDITIONAL RESOURCES U.S. Census Bureau, What Is the American Community Survey? <www.census.gov/programs-surveys/acs/about.html> U.S. Census Bureau, ACS Data Tables and Tools <www.census.gov/acs/www/data/data-tables -and-tools/> U.S. Census Bureau, Understanding and Using American Community Survey Data: What All Data Users Need to Know <www.census.gov/programs-surveys/acs/guidance /handbooks/general.html> U.S. Census Bureau, Census Business Builder (CBB) <www.census.gov/data/data-tools/cbb.html> U.S. Census Bureau, Data.census.gov Resources <www.census.gov/data/what-is-data-census-gov.html> U.S. Census Bureau, ACS Data Releases <www.census.gov/programs-surveys/acs/news /data-releases.html> U.S. Census Bureau, Geography and ACS <www.census.gov/programs-surveys/acs /geography-acs.html> U.S. Census Bureau, State Data Center (SDC) Program <www.census.gov/about/partners/sdc.html> ACS Online Community <https://acsdatacommunity.prb.org/> Understanding and Using American Community Survey Data 25 What State and Local Government Users Need to Know 25 U.S. Census Bureau