Using Bayesian Methods to Control for Spatial Autocorrelation in Environmental Justice Research: An Illustration Using Toxics Release Inventory Data for a Sunbelt County

Abstract

ABSTRACT:Many previous environmental justice (EJ) studies have argued that there is disproportionate collocation of environmental disamenities with racial and ethnic minorities, even holding constant other factors such as income and political action. However, most of the EJ studies do not account for the presence of spatial autocorrelation, especially those that also include nonnormal distributions. Using the location of new Toxics Release Inventory facilities (TRIFs) in Maricopa County, Arizona in the 1990s, we illustrate a finding of spatial autocorrelation and the use of Bayesian spatial models to accommodate the issue. The results show that the relationship between Asian minority status in a census tract and new TRIF establishments found with regression models does not remain statistically significant once spatial autocorrelation is accounted for. Instead, three variables, the percentage of American Indians in the tract, population density, and the percentage of residents aged 55–74, statistically significantly explained new TRIF establishments. This illustrates that failure to control for spatial autocorrelation can lead to incorrect policy understanding.