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Geospatial methods are increasingly used to evaluate environmental exposures in epidemiologic studies. Geospatial data are often acquired from different sources and time periods and at different spatial resolutions. This dissertation presents methods for combining datasets that do not overlap in space and, or time and are essential for the accurate quantification of metrics used in Environmental Health Sciences. In three chapters, we demonstrated and compared advances in small-area geospatial methods used to reduce misalignment and misclassification of key variables (e.g., exposure).First, we showed how small-area analyses of COVID-19 can benefit from spatial aggregation to account for areal misalignment. In an analysis of the association between air pollution levels and COVID-19 incidence and mortality, we used residential building footprints to combine misaligned COVID-19 outcomes recorded at the neighborhood level, population demographics recorded at the census-tract level, and NO2 interpolated surfaces recorded at 30-meter grids. We found NO2 to be positively associated with COVID-19 incidence and mortality for neighborhoods in Los Angeles.Second, we used data acquired through the PASTA-LA study to attribute daily green space exposure for physically-active spaces (PASs) where activity, location, and green space data were misaligned. We used tracking data from accelerometers and smartphones to attribute green-space exposure using 21 geospatial methods. We found that exposures attributed to home-address buffers, a commonly used method, can result in exposure misclassification. We also found a large range in correlation depending on tracking device used and the way physical activity was categorized across the 21 methods, suggesting that the method selected is key to the findings.Finally, third, also using PASTA-LA data, we studied the association between heat exposure and physical activity in which temperature, green space, and participant-level covariates were mis

Geospatial methods are increasingly used to evaluate environmental exposures in epidemiologic studies. Geospatial data are often acquired from different sources and time periods and at different spatial resolutions. This dissertation presents methods for combining datasets that do not overlap in space and, or time and are essential for the accurate quantification of metrics used in Environmental Health Sciences. In three chapters, we demonstrated and compared advances in small-area geospatial methods used to reduce misalignment and misclassification of key variables (e.g., exposure).First, we showed how small-area analyses of COVID-19 can benefit from spatial aggregation to account for areal misalignment. In an analysis of the association between air pollution levels and COVID-19 incidence and mortality, we used residential building footprints to combine misaligned COVID-19 outcomes recorded at the neighborhood level, population demographics recorded at the census-tract level, and NO2 interpolated surfaces recorded at 30-meter grids. We found NO2 to be positively associated with COVID-19 incidence and mortality for neighborhoods in Los Angeles.Second, we used data acquired through the PASTA-LA study to attribute daily green space exposure for physically-active spaces (PASs) where activity, location, and green space data were misaligned. We used tracking data from accelerometers and smartphones to attribute green-space exposure using 21 geospatial methods. We found that exposures attributed to home-address buffers, a commonly used method, can result in exposure misclassification. We also found a large range in correlation depending on tracking device used and the way physical activity was categorized across the 21 methods, suggesting that the method selected is key to the findings.Finally, third, also using PASTA-LA data, we studied the association between heat exposure and physical activity in which temperature, green space, and participant-level covariates were mis

The turn of the millennium has been accompanied by a rapid growth in data collection along with an increasing ability to store, manipulate and analyze it. In tandem with this development in technology and surveillance, there has been a growing understanding of the importance of the socio-physical environment on population behavior and human health. We capitalize on this progress to develop a methodology by which we develop two macro scale datasets, one national and one state-wide, to support the efforts of the Centers for Disease Control and Prevention's Environmental Public Health Tracking Network (EPHTN) in understanding two important health risks: smoking and obesity. Moreover, we use new geographic information science techniques, spatial statistics methodologies and machine learning algorithms to gain a better understanding of the relationship between spatial patterns in physical and socio-demographic characteristics and health risks. Specifically, we address three specific aims: (1) To use current data systems to develop national small-area predictions of adult smoking and obesity for the EPHTN and research; (2) To evaluate current data systems and spatial analysis tools available to describe the within-school environment and the school-neighborhood socio-physical characteristics of California's public schools thought to influence childhood obesity, and use these to develop a comprehensive multilevel dataset for the EPHTN and research; (3) To test the utility of the dataset developed in aim 2 by applying it to an analysis used to increase the understanding of the relationship between the school environment and childhood obesity by examining the relative importance of school attributes. This analysis determines that current data collection systems provide a valuable resource which we combine for the ease of future research use. We demonstrate that using the spatial structure and socio-demographic patterns of health risks, we are able to downscale adult smoking a

The turn of the millennium has been accompanied by a rapid growth in data collection along with an increasing ability to store, manipulate and analyze it. In tandem with this development in technology and surveillance, there has been a growing understanding of the importance of the socio-physical environment on population behavior and human health. We capitalize on this progress to develop a methodology by which we develop two macro scale datasets, one national and one state-wide, to support the efforts of the Centers for Disease Control and Prevention's Environmental Public Health Tracking Network (EPHTN) in understanding two important health risks: smoking and obesity. Moreover, we use new geographic information science techniques, spatial statistics methodologies and machine learning algorithms to gain a better understanding of the relationship between spatial patterns in physical and socio-demographic characteristics and health risks. Specifically, we address three specific aims: (1) To use current data systems to develop national small-area predictions of adult smoking and obesity for the EPHTN and research; (2) To evaluate current data systems and spatial analysis tools available to describe the within-school environment and the school-neighborhood socio-physical characteristics of California's public schools thought to influence childhood obesity, and use these to develop a comprehensive multilevel dataset for the EPHTN and research; (3) To test the utility of the dataset developed in aim 2 by applying it to an analysis used to increase the understanding of the relationship between the school environment and childhood obesity by examining the relative importance of school attributes. This analysis determines that current data collection systems provide a valuable resource which we combine for the ease of future research use. We demonstrate that using the spatial structure and socio-demographic patterns of health risks, we are able to downscale adult smoking a