# 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
