Graduation Year


Document Type




Degree Granting Department


Major Professor

Jayajit Chakraborty, Ph.D.

Committee Member

Pratyusha Basu, Ph.D.

Committee Member

Steven Reader, Ph.D.


environmental justice, air pollution, NATA, geographically weighted regression, public health


A large number of quantitative studies have examined social inequities in the geographic distribution of air pollution. Although previous research has made strides towards understanding the nature and extent of inequities, they have been limited methodologically in three ways. First, the presence of pollutants have been rarely linked to their adverse health effects, with many studies using proximity to sources as a proxy for risk. Second, there has been a tendency to study a single pollution source instead of assessing multiple types of sources. Finally, conventional statistical methods such as multivariate regression have been limited by their inability to discern spatial variations in the relationships between dependent and explanatory variables.

This thesis addresses these gaps in environmental justice analysis of air pollution by using data from U.S. Environmental Protection Agency's 1999 National-Scale Air Toxics Assessment in combination with 2000 U.S. Census data to evaluate inequities in the geography of cancer risks from hazardous air pollutants in Florida. The objective is to determine if there are racial/ethnic inequities in the distribution of estimated cancer risks from outdoor exposure to point and mobile sources of air pollutants, after controlling for well-documented contextual variables. The first phase of the study utilizes traditional correlation and regression techniques to reveal that cancer risk from most air pollution sources are distributed inequitably with respect to race, ethnicity, and socioeconomic state. In the second phase, geographically weighted regression is used along with choropleth mapping to explore the spatial nonstationarity of regression model parameters and geographic variations in the statistical association between cancer risks and various explanatory variables. Results indicate that while Black and Hispanic proportions remain consistent indicators of cancer risk from most pollution sources, these relationships vary across space within Florida. This thesis contributes to environmental justice analysis by demonstrating that conventional multivariate regression can hide important local variations in the relationships between environmental risk and explanatory variables such as race, ethnicity, and socioeconomic status. Since this spatial nonstationarity can be significant within an entire region or a single urban area, understanding its nature and extent is imperative to advancing environmental justice goals.