The Distribution of Environmental Equity: Exploring Spatial Nonstationarity in Multivariate Models of Air Toxic Releases

Abstract

Conventional multivariate regression can hide important local variations in the relationships among independent and dependent variables in models of environmental equity. Geographically weighted regression (GWR), in combination with choropleth mapping, can reveal this spatial nonstationarity and shed light on its form. We use GWR, in combination with conventional univariate and multivariate statistics, to model the density of air toxic releases in New Jersey, as listed in the U.S. Environmental Protection Agency's Toxic Release Inventory (TRI). The GWR analysis shows that the relationships among race, class, employment, urban concentration, and land use with air toxic release density in New Jersey vary significantly over space. Generally, there is a positively significant relationship of minorities with air toxic releases over a large swath of urban and suburban New Jersey, although this pattern is not evident for all urban areas. Northeast New Jersey, the most densely populated part of the state, contains areas of both significantly positive and negative relationships between concentrations of minorities and air toxic releases. The association of minorities with concentrations of air toxic releases, where observed, is often mediated by other factors, though the role of these mediating factors also varies from place to place. In some of these areas the minority–air-toxic-release association is mediated by high poverty rates, in other areas, by the presence of industrial, commercial, and transportation land uses.

Key Words:

• environmental justice
• environmental racism
• geographically weighted regression
• spatial statistics
• spatial analysis

Acknowledgments

The authors would like to thank Stewart Fotheringham, Chris Brunsdon, and Martin Charlton for developing and distributing the GWR software. Thanks also to Gary Gaile for providing helpful comments on a previous draft.

Notes

**p<0.01

*p< 0.05,

***p <0.005;

t-values appear in parentheses.

*p<0.05,

**p<0.01,

***p<0.005;

t-values appear in parentheses.

**p<0.01.