Spatial Statistical Analysis Applications in Environmental Justice: Assessing the Relationships Between Exposure and Social Vulnerability

Abstract

Background: Two cancer clusters near potential contaminate sites have recently been identified in Northeast Houston, Texas. A Creosote plume from the Englewood Rail Yards wood treatment processes during the early 1900s to the 1980s is the suspected cause. Methods: We present a case study that demonstrates how complex spatial analysis and Geographic Information Systems (GIS) can be used to identify spatial relationships between soil contaminant risk variables (total incremental lifetime cancer risk, Total concentration of polycyclic aromatic hydrocarbons (PAH), Benzo(a)pyrene, Naphthalene, Pyrene) and the CDC’s 2018 Social Vulnerability Index. Results: The results show that high-density clusters of high concentrations of all five risk variables were found surrounding the Englewood Rail Yard, and low-density clusters of low concentrations were found at the edges of the study site, which was expected. Clusters were also positively correlated with low socioeconomic status, aligning with environmental justice literature. Discussion: Complex spatial analyses that account for spatial autocorrelation produce more accurate results. Spatial representation of pollutants allows researchers to analyze the breadth of contamination and communicate findings to a broader audience. Further research is needed to incorporate a larger study area, investigate updated socioeconomic variables (2020 U.S. Census), and investigate other PAH pollutants. Conclusion: Spatial clustering analysis is a productive means to identify the relationship between pollutant risks/sources of environmental contamination and social vulnerability. Prospective designs implementing this technique are needed within environmental justice research.