Increasing evidence suggests that the environment is related to many public health challenges. Unequal distributions of services and resources needed for healthy lifestyles may contribute to increasing levels of health disparity. However, empirical studies are not sufficient to understand the relationship between health disparity and the built environment. This dissertation examines how health disparity are associated with the built environment and if the environmental conditions that support physical activity and healthy diet are associated with lower health disparity. This research uses a multidisciplinary approach, drawing from urban planning, regional economics and public health. The data came from the Behavioral Risk Factor Surveillance System, and the GIS derived environmental data and the 608-respondent survey data from a larger study conducted in urbanized King County, Washington. Health disparity was measured with the Gini-coefficient, and health status and obesity were used as indicators of health. Hot spot analysis was used to identify the spatial aggregations of high health disparity, and multiple regression models identified the environmental correlates of health disparity. The overall trend showed that disparity has increased in most states in the US over the past decade and the southern states showed the highest disparity levels. Strong spatial autocorrelations were found for disparities, indicating that disparity levels are not equally distributed across different geographic areas. From the multivariate analyses estimating disparity levels, spatial regression models significantly improved the overall model fit compared to the ordinary least-square models. Areas with more supportive built environments for physical activity had lower health disparities, including proximity to downtown (+) and access to parks (+), day care centers (+), offices (+), schools (+), theaters (+), big box shopping centers (-), and libraries (-). Overall results showed that the built environment, compared to the personal factors, was more strongly correlated with health disparities. This study brings attention to the problem of health disparity in the US, and provides evidence supporting the existence of spatial disparity in the environmental support for a healthy lifestyle. Further research is needed to better understand environmental and socioeconomic conditions associated with health disparity among more diverse population groups and in different environmental settings.

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Indiana University-Purdue University Indianapolis (IUPUI) / The objective of this research is to examine the development of the California alcohol outlets over time and the relationship between neighborhood characteristics and densities of the alcohol outlets. Two types of advanced analyses were done after the usual preliminary description of data. Firstly, fixed and random effects linear regression were used for the county panel data across time (1945-2010) with a dummy variable added to capture the change in law regarding limitations on alcohol outlets density. Secondly, a Bayesian spatio-temporal Poisson regression of the census tract panel data was conducted to capture recent availability of population characteristics affecting outlet density. The spatial Conditional Autoregressive model was embedded in the Poisson regression to detect spatial dependency of unexplained variance of alcohol outlet density. The results show that the alcohol outlets density reduced under the limitation law over time. However, it was no more effective in reducing the growth of alcohol outlets after the limitation was modified to be more restrictive. Poorer, higher vacancy rate and lower percentage of Black neighborhoods tend to have higher alcohol outlet density (numbers of alcohol outlets to population ratio) for both on-sale general and off-sale general. Other characteristics like percentage of Hispanics, percentage of Asians, percentage of younger population and median income of adjacency neighbors were associated with densities of on-sale general and off sale general alcohol outlets. Some regions like the San Francisco Bay area and the Greater Los Angeles area have more alcohol outlets than the predictions of neighborhood characteristics included in the model.