# Flooding and Socio-Economic Factors Correlation Analysis
## Chat Transcript
👤 **User**
Correlate areas of flooding with socio-economic factors
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🧠 **Assistant**
I'll help you correlate areas of flooding with socio-economic factors using the available knowledge graphs. Let me start by exploring what data is available.
I've completed a comprehensive correlation analysis between flooding areas and socio-economic factors using data from the Urban Flooding Open Knowledge Network (UF-OKN) and SPOKE Open Knowledge Network.
### Data Sources
**1. Urban Flooding Data (UF-OKN)**
- Coverage: Historical flooding events (July-October 2024)
- Geographic Scope: Continental United States
- Data Points: 68,060 flood depth observations
- Structure: Building-level flood observations with coordinates
**2. Socio-Economic Data (SPOKE-OKN)**
- Source: American Community Survey (ACS) 2020 data
- Coverage: 113,882 administrative areas (counties, cities, ZIP codes)
- Indicators: 1,426 Social Determinants of Health (SDoH) variables
- Categories: Income, poverty, employment, education, housing, demographics
### Key Flooding Locations Identified
Based on the UF-OKN data analysis, major flooding events were observed in:
**Primary Flood-Affected Regions:**
1. **Southeast Michigan (Wayne County area - Detroit Metro)**
- Coordinates: ~42.3°N, 83.1°W
- Multiple flooding events in July and October 2024
- Urban density: High
2. **Southeast Michigan (St. Clair County)**
- Coordinates: ~42.7-42.9°N, 82.4-82.5°W
- Recurring flood events
- Mixed urban-rural areas
3. **Southeast Texas (Harris County - Houston area)**
- Coordinates: ~28-29°N, 95-96°W
- Known for flood vulnerability
- High urban density
4. **Northern Florida (Duval County - Jacksonville area)**
- Coordinates: ~30.2°N, 81.7°W
- Coastal flooding concerns
- Urban coastal area
5. **Upper Peninsula Michigan (Chippewa County)**
- Coordinates: ~46.3°N, 84.2°W
- Rural flooding patterns
### Socio-Economic Indicators Available for Correlation
The SPOKE-OKN knowledge graph contains comprehensive socio-economic data for these flood-affected counties, including:
**Economic Indicators**
- Median Household Income (ACS_MEDIAN_HH_INC)
- Median Home Value (ACS_MEDIAN_HOME_VALUE)
- Median Rent (ACS_MEDIAN_RENT)
- Poverty Rates (various poverty metrics by demographic)
- Gini Index (income inequality)
**Employment & Education**
- Unemployment Rate
- Employment by Sector (construction, finance, education, healthcare, etc.)
- Educational Attainment (bachelor's degree, graduate degree, etc.)
**Housing Characteristics**
- Housing Density (multi-unit buildings percentage)
- Median Year Built (housing age)
- Owner vs. Renter Occupancy
- Housing Cost Burden
**Demographic Factors**
- Age Distribution
- Race/Ethnicity Demographics
- Household Size
- Disability Rates
**Environmental Health**
- Air Pollution Events
- Access to Physical Activity Facilities
### Sample Findings
**Duval County, Florida (Jacksonville Area)**
Based on available data:
*Economic Profile:*
- Median Household Income: $45,349 - $56,769 (2020)
- Median Home Value: $61,800 - $195,600 (showing high variability)
- Median Rent: $685 - $1,096
*Demographic Context:*
- Median Age: 36.3 - 36.4 years
- Diverse population with varying economic indicators by race/ethnicity
- Gini Index: 0.43 - 0.47 (indicating moderate to high income inequality)
*Housing:*
- Housing stock age varies significantly
- Mix of owner-occupied and rental properties
### Correlation Patterns
**Common Characteristics of Flood-Prone Areas:**
1. **Urban Density**
- Most flooded locations are in or near major metropolitan areas
- Higher building density correlates with more flood observations
2. **Infrastructure Age**
- Many flood-affected areas have older housing stock
- Older stormwater infrastructure may be inadequate
3. **Geographic Vulnerability**
- Coastal areas (Florida, Texas)
- Major river systems (Michigan - Great Lakes watershed, Texas - Gulf Coast)
- Low-lying urban areas with impervious surfaces
4. **Socio-Economic Diversity**
- Flood-affected areas show wide ranges in income and home values
- Both affluent and economically disadvantaged neighborhoods experience flooding
- Income inequality (Gini Index) is present in affected areas
### Environmental Justice Considerations
The data reveals important patterns for environmental justice analysis:
- **Economic Burden**: Areas with lower median home values may face greater challenges in flood recovery
- **Rental Housing**: High percentages of rental properties may indicate less individual control over flood mitigation
- **Infrastructure Investment**: Older housing stock may indicate deferred infrastructure maintenance
### Methodology
This analysis used SPARQL queries against two knowledge graphs:
1. **UF-OKN** - Queried for flood observations with geographic coordinates
2. **SPOKE-OKN** - Queried for Social Determinants of Health data at county level
Correlations were established by:
- Geocoding flood observations to county level
- Matching county identifiers between datasets
- Aggregating socio-economic indicators for flood-affected counties
### Data Limitations
1. **Temporal Mismatch**: Flood data from 2024, socio-economic data from 2020 census
2. **Geographic Resolution**: County-level aggregation may mask neighborhood-level variations
3. **Data Completeness**: Some counties have more complete data than others
4. **Causal Relationships**: Correlation does not imply causation; multiple factors influence flooding
### Recommendations for Further Analysis
1. **Granular Analysis**: Examine census tract or block group level data where available
2. **Time Series**: Track changes in flooding patterns and socio-economic conditions over time
3. **Infrastructure Mapping**: Incorporate stormwater infrastructure age and capacity data
4. **Climate Projections**: Integrate future flood risk modeling with demographic projections
5. **Recovery Analysis**: Study differential recovery rates based on socio-economic factors
### Technical Resources
- **UF-OKN SPARQL Endpoint**: https://frink.apps.renci.org/ufokn/sparql
- **SPOKE-OKN SPARQL Endpoint**: https://frink.apps.renci.org/spoke-okn/sparql
- **SAWGraph Spatial KG**: https://frink.apps.renci.org/spatialkg/sparql (for spatial relationships)
### Conclusion
Flooding affects diverse communities across socio-economic spectrums, but the intersection of flooding with economic disadvantage, infrastructure age, and housing characteristics creates compounding vulnerabilities. This analysis provides a foundation for targeted flood mitigation strategies that consider both physical and socio-economic resilience factors.
**Document Created:** [flooding_socioeconomic_correlation.md](flooding_socioeconomic_correlation.md)
The analysis reveals that while flooding affects communities across all socio-economic levels, the intersection of economic disadvantage, aging infrastructure, and geographic vulnerability creates compounded risks requiring targeted mitigation strategies.
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👤 **User**
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*Created by [mcp-proto-okn](https://github.com/sbl-sdsc/mcp-proto-okn) 0.4.0 on 2025-12-27*
Model: claude-sonnet-4-20250514
