Mapping Vulnerability: A Geospatial Analysis of Heat-Related Illnesses and Cooling Center Accessibility in Texas

• In Event: Poverty and Environmental Inequity: Land, Heat, and Energy

Abstract

Research Objectives:

This study aims to assess the geographic distribution of heat-related illness (HRI) in Texas and evaluate access to cooling centers as a form of public health infrastructure. Specifically, the project seeks to 1) identify spatial hotspots of HRI across Texas, 2) determine how population, temperature, and social vulnerability predict access to cooling centers; and 3) recommend optimal locations for new centers to better serve high-risk populations.

 

Study Design and Methods:

I conducted a geospatial study using the 2016-2023 Texas Emergency Department Public Use Data File from the Texas Health Care Information Collection Center. I defined HRI as occurring when a patient’s record showed the presence of an International Classification of Diseases (ICD) injury diagnosis code for the effects of heat and light for primary diagnosis (ICD-10 codes T67, X30, W92). Using geocoded cooling center locations in Texas, average temperature data, and Social Vulnerability Index (SVI), I conducted a combination of GIS and statistical modeling tools in ArcGIS Pro. Hotspot detection was performed using Getis-Ord Gi* to identify statistically significant clusters of HRI ED visits. Accessibility was assessed using buffer analysis (2 km, 5 km, 10 km) and spatial overlays. Logistic regression and Multiscale Geographically Weighted Regression (MGWR) were applied to examine how local population, average temperature, and social vulnerability (SVI) influence the likelihood of having a cooling center within a ZIP Code Tabulation Area (ZCTA). A weighted-overlay site suitability model was used to identify optimal locations for future cooling centers based on multiple criteria.

Population Studied:

The study population included patients with a primary HRI diagnosis at zip-code level in Texas (N=2,938). The study also included a geocoded dataset of 474 known cooling center locations across Texas.

Principal Findings:

The analysis revealed that HRI is highly clustered in urban corridors, including Houston, Dallas–Fort Worth, Austin, and San Antonio. Despite this, only 9.1% of HRI hotspots lie within 5 km of a cooling center. Further, one-third of Texas residents live more than 5 km away from the nearest cooling center. More than 75,600 square kilometers of hotspot territory lack adequate access. Logistic regression showed that both higher population and higher average temperature significantly increase the likelihood of cooling center presence (p<0.001), while social vulnerability was not a significant statewide predictor (p = 0.484). MGWR revealed spatial heterogeneity in these relationships, with southern and rural areas showing significant cooling center equity gaps. A site suitability model identified 218 potential new locations for cooling centers, potentially improving access for tens of thousands of underserved residents.

 

Conclusion:

Cooling centers in Texas are disproportionately located in more populated and hotter areas, but not necessarily in more socially vulnerable ones. This uneven distribution creates a clear equity gap in emergency heat protection.

Policy Implications:

Public health and emergency management agencies should adopt a data-driven approach to place cooling centers, prioritizing both exposure risk and vulnerability. Incorporating spatial analysis into preparedness planning will ensure that resources are equitably distributed and more resilient to rising temperatures driven by climate change.

Author

• Gogoal Falia, Texas A&M University
  Presenting Author