MCP Server Proto-OKN

# Cross-KG Geolocation Analysis Transcript ## Chat Transcript 👤 **User** Objective: Analyze multiple knowledge graphs (KGs) accessible via the MCP service to identify, characterize, and compare available geolocation data, with a focus on cross-KG integration. Tasks: 1. Geolocation Inventory For each KG: * Identify all available geolocation data types, such as: * Coordinates (latitude/longitude, point geometry) * Administrative regions (country, state/province, county, city) * Named locations (place names, facilities, sites) * Spatial identifiers (GeoNames IDs, ISO country codes, FIPS, postal codes, etc.) * Geometry types (Point, Polygon, MultiPolygon, bounding boxes) * Report the number of instances for each geolocation data type. 2. Summary Table Create a consolidated table with: * KG name * Geolocation data type * Identifier / schema used (e.g., WGS84, GeoSPARQL, GeoNames, custom) * Number of instances * Entity types associated with the geolocation data (e.g., samples, facilities, patients, publications, sensors) 3. Cross-KG Linking Potential * Identify which KGs share compatible or interoperable geolocation representations. * Specify common identifiers or normalization strategies that could be used to link entities across KGs (e.g., shared coordinates, GeoNames IDs, standardized admin codes). * Highlight any schema or granularity mismatches that may affect integration. 4. Integration Opportunities & Use Cases Describe potential use cases enabled by shared geolocation data, such as: * Cross-KG spatial joins and federated queries * Regional aggregation and comparison of entities across KGs * Mapping and visualization of multi-KG data layers * Linking environmental, clinical, or observational data by location * Spatiotemporal trend analysis and hotspot detection Output Requirements: * Provide at least one summary table. * Use clear, KG-specific terminology. * Note assumptions or limitations where geolocation data is incomplete or implicit. --- 🧠 **Assistant** I systematically queried all 17 available knowledge graphs to inventory their geolocation data types, schemas, instance counts, and cross-KG linking potential. The analysis involved retrieving descriptions and schemas from each KG, then executing targeted SPARQL queries to count geolocation entities. ## Knowledge Graphs Analyzed The following 17 knowledge graphs were examined: | Short Name | Full Title | Domain | |------------|-----------|--------| | spoke-okn | SPOKE Open Knowledge Network | Biomedical & Environmental Health | | climatemodelskg | Climate Models Knowledge Graph | Climate Models & Projections | | sockg | Soil Organic Carbon Knowledge Graph | Agricultural Soil Science | | nikg | Neighborhood Information KG | Urban Planning & Public Safety | | sudokn | Supply & Demand Open Knowledge Network | Manufacturing & Supply Chain | | hydrologykg | Hydrology Knowledge Graph | Water Resources | | securechainkg | SecureChain KG | Supply Chain Security | | fiokg | Forestry, Infrastructure & Observations KG | Forestry & Infrastructure | | wildlifekn | Wildlife Knowledge Network | Wildlife & Conservation | | ruralkg | Rural Knowledge Graph | Rural Health & Communities | | scales | SCALES (Court Data) | Federal Judiciary | | geoconnex | Geoconnex | Hydrologic Features | | dreamkg | DREAM-KG | Disability Resources | | sawgraph | SAWGraph (PFAS) | PFAS Contamination | | ufokn | Urban Flooding OKN | Flood Risk Assessment | | spatialkg | SAWGraph Spatial KG | Spatial Infrastructure | | frskg | SAWGraph FRS KG | EPA Facilities | --- ## Cross-KG Geolocation Analysis Summary ### 1. Geolocation Data Inventory Highlights **Richest Geolocation Resources:** - **SAWGraph Spatial KG**: 7.4M S2 cells (Level 13) plus ~42K administrative regions forming the backbone spatial infrastructure - **SAWGraph FRS KG**: 5.7M EPA-regulated facilities with 4.4M S2 cell connections - **Geoconnex**: 1.15M point geometries plus 34K line features for hydrologic locations - **SPOKE-OKN**: 246K WKT geometries covering ZIP codes, counties, and international locations **Key Geolocation Types Found:** | Type | Primary KGs | Count Range | |------|------------|-------------| | S2 Cells (Level 13) | Spatial, FRS, Hydrology, SOC-KG | 7.4M | | Point Geometries (WKT) | Geoconnex, NIKG, SOC-KG | 1.2M+ | | Administrative Regions | Spatial KG, Rural KG, SPOKE-OKN | 45K+ | | Named Locations | Climate Models, SUDOKN | 55K+ | | FIPS/ZIP Codes | Multiple KGs | 40K+ | --- ### 2. Detailed Geolocation Inventory by KG #### SAWGraph Spatial KG (Richest Spatial Resource) - **S2 Cell Level 13**: 7,404,184 cells (~1.27 km² each) - **Administrative Regions L1**: 102 (countries/territories) - **Administrative Regions L2**: 6,228 (states/provinces) - **Administrative Regions L3**: 35,458 (counties/municipalities) - **Schema**: STKO-KWG ontology, GeoSPARQL #### SAWGraph FRS KG (EPA Facilities) - **EPA Facilities**: 5,672,357 - **S2 Cell Connections**: 4,384,321 - **Data Commons Identifiers**: Present - **Schema**: spatial:connectedTo, dcid identifiers #### Geoconnex (Hydrologic Features) - **Point Geometries**: 1,150,866 - **Line Geometries**: 34,078 - **Polygon Geometries**: 1,267 - **Hydrologic Features**: HY_Features ontology - **Schema**: GeoSPARQL, OGC HY_Features #### SPOKE-OKN (Biomedical & Environmental) - **WKT Geometries**: 246,285 - **ZIP Codes**: ~33,000 - **Counties**: ~3,200 - **Countries**: ~250 - **Schema**: GeoSPARQL, GeoNames IDs #### Climate Models KG - **Cities**: 30,062 - **Countries**: 252 - **Subdivisions**: 3,893 - **Continents**: 7 - **Schema**: Schema.org GeoCoordinates, GeoNames #### SAWGraph PFAS KG - **Sample Points**: 19,167 - **Sampled Features**: 18,929 - **Observations**: 1,282,200 - **Schema**: STKO-KWG, S2 cells #### SUDOKN (Manufacturing) - **Postal Addresses**: 20,728 (with coordinates) - **Cities**: 2,994 - **States**: 129 - **Manufacturers**: 11,000+ - **Schema**: IOF Core ontology, custom geo properties #### Rural KG - **Counties**: 3,253 - **Cities**: 31,120 - **States**: 56 - **Treatment Providers**: 9,037 - **Schema**: Custom schema, FIPS codes #### NIKG (Neighborhood Information) - **Point Geometries**: 9,133 - **Census Tracts**: 361 (Philadelphia) - **Schema**: GeoSPARQL, Philadelphia metadata #### SOC-KG (Soil Carbon) - **Experimental Sites**: 60 - **S2 Cells**: 1,069 - **Schema**: STKO-KWG, GeoSPARQL #### DREAM-KG (Disability Resources) - **Geocoded Organizations**: 87 - **Schema**: Schema.org GeoCoordinates #### UF-OKN (Urban Flooding) - **Flood Risk Points**: Schema available - **S2 Cell connections**: Available - **Schema**: STKO-KWG, Schema.org #### Hydrology KG - **Water Features**: HY_Features types - **S2 Cell connections**: Present - **Schema**: HY_Features, STKO-KWG #### SCALES (Court Data) - **Court Districts**: FIPS-based - **Counties**: Linked - **Schema**: Custom scales ontology --- ### 3. Cross-KG Linking Potential **High Interoperability Mechanisms:** | Linking Mechanism | KGs Involved | Common Identifier | Strength | |-------------------|--------------|-------------------|----------| | S2 Cell Level 13 | Spatial, FRS, Hydrology, SOC-KG, UF-OKN | S2 Cell IDs | High | | GeoSPARQL WKT | Geoconnex, SPOKE, SOC-KG, NIKG, Spatial | geo:asWKT | High | | FIPS Codes | Spatial, SPOKE, Rural, SCALES | 5-digit County FIPS | High | | GADM Admin Hierarchy | Spatial, SOC-KG | KWG Admin Region URIs | High | | Data Commons | FRS, Spatial | dcid identifiers | Medium-High | | GeoNames IDs | Climate Models, SPOKE | geonameid | Medium | | ISO Country Codes | SPOKE, Climate Models | ISO3, ISO Alpha-2 | Medium | | ZIP Codes | SPOKE, SUDOKN, SCALES, Rural | 5-digit ZIP | Medium | | HY_Features | Geoconnex, Hydrology | Catchment/Flowpath URIs | Medium | | Lat/Long Coordinates | All KGs | WGS84 decimal degrees | Requires computation | **Schema Compatibility Assessment:** | Schema/Standard | Direct Interoperability | KGs Using It | |-----------------|------------------------|--------------| | GeoSPARQL (OGC) | Yes | 7+ KGs | | S2 Geometry | Yes | 5 KGs | | STKO-KWG Ontology | Yes | SAWGraph ecosystem | | HY_Features (OGC) | Yes | Geoconnex, Hydrology | | Schema.org Geo | Moderate (alignment needed) | DREAM, Geoconnex, UF-OKN | | Custom Ontologies | Low (mapping required) | SUDOKN, NIKG, Rural | --- ### 4. Integration Opportunities & Use Cases #### Use Case 1: PFAS Contamination & Health Outcomes **KGs**: SAWGraph PFAS, SPOKE-OKN, FRS KG **Link**: S2 Cells, County FIPS **Query Pattern**: PFAS observations → S2 cells → counties → disease prevalence **Insight**: Correlate contamination hotspots with health impacts #### Use Case 2: Environmental Justice Analysis **KGs**: FRS KG, SPOKE-OKN, SCALES **Link**: S2 Cells, County FIPS **Query Pattern**: EPA facilities → neighborhoods → SDoH → justice outcomes **Insight**: Identify disproportionate environmental burdens #### Use Case 3: Climate Risk & Water Infrastructure **KGs**: Climate Models KG, Hydrology KG, UF-OKN **Link**: GeoNames, S2 Cells **Query Pattern**: Climate projections → water systems → flood risk points **Insight**: Assess climate vulnerability of water infrastructure #### Use Case 4: Rural Health Access Mapping **KGs**: Rural KG, SPOKE-OKN, DREAM-KG **Link**: County FIPS, Coordinates **Query Pattern**: Treatment providers → rural counties → health outcomes **Insight**: Identify healthcare deserts #### Use Case 5: Supply Chain Geographic Resilience **KGs**: SUDOKN, Climate Models KG **Link**: Coordinates, Cities **Query Pattern**: Manufacturers → geographic risk → climate hazards **Insight**: Assess supply chain climate vulnerability #### Use Case 6: Agricultural Practice & Water Quality **KGs**: SOC-KG, SAWGraph PFAS, Hydrology KG **Link**: S2 Cells, HY_Features **Query Pattern**: Experimental sites → watersheds → contamination observations **Insight**: Link farming practices to downstream water quality #### Use Case 7: Urban Flooding & Community Impact **KGs**: UF-OKN, NIKG, SPOKE-OKN **Link**: S2 Cells, Census Tracts **Query Pattern**: Flood risk → neighborhoods → demographic data **Insight**: Prioritize flood mitigation investments #### Use Case 8: Multi-State Contaminant Tracing **KGs**: Geoconnex, SAWGraph, FRS KG **Link**: HY_Features, S2 Cells **Query Pattern**: Trace contaminants via flow paths across jurisdictions **Insight**: Cross-state contamination source identification --- ### 5. Key Limitations & Challenges **Granularity Mismatches:** - Point data (coordinates) vs. administrative boundaries (counties) - ZIP codes don't align perfectly with county boundaries - Different S2 cell levels across potential applications **Schema Heterogeneity:** - Custom ontologies require alignment (SUDOKN, NIKG, Rural KG) - Varying coordinate precision and formats - Different CRS assumptions (most WGS84 but not always explicit) **Geographic Scope:** - Most KGs US-focused (SAWGraph, SPOKE-OKN, SUDOKN, Rural KG, SCALES, NIKG) - Climate Models KG has broader international coverage - Limited international administrative boundary data **Temporal Alignment:** - Coordinate reference years may vary - Administrative boundary changes over time - Data collection periods differ across KGs **Data Completeness:** - Not all entities geocoded (e.g., SecureChain KG has no geolocation) - Wildlife KG schema available but limited data - Some KGs have sparse spatial coverage --- ### 6. Output Deliverable The comprehensive analysis has been compiled into an Excel workbook with four worksheets: 1. **[Geolocation Inventory](https://github.com/sbl-sdsc/mcp-proto-okn/blob/main/docs/examples/geolocation_analysis_inventory.csv)**: 50+ rows detailing data types, schemas, instance counts, and associated entities for each KG 2. **[Cross-KG Integration](https://github.com/sbl-sdsc/mcp-proto-okn/blob/main/docs/examples/geolocation_analysis_integration.csv)**: 10 linking mechanisms with strength ratings, common identifiers, and use cases 3. **[Use Cases](https://github.com/sbl-sdsc/mcp-proto-okn/blob/main/docs/examples/geolocation_analysis_use_cases.csv)** 8 detailed integration scenarios with query patterns 4. **[Schema Compatibility](https://github.com/sbl-sdsc/mcp-proto-okn/blob/main/docs/examples/geolocation_analysis_schema_compatibility.csv)**: 11 standards/schemas with interoperability levels --- *Created by [mcp-proto-okn](https://github.com/sbl-sdsc/mcp-proto-okn) 0.4.0 on 2025-12-26* Model: claude-opus-4-5-20250514