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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 misaligned. We showed that heat exposure was associated with increased physical activity and that exposure to green space modified this association in only some models. In this chapter, and in all chapters of this dissertation, we demonstrated how choice in geospatial approach to spatiotemporal misalignment can yield different study results.

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 and obesity prevalence to the ZIP code and census tract levels for the conterminous United States, and develop five-year predictions for these for the four quinquennia in the 1991-2010 time period. Lastly, we confirm the utility of the school dataset and determine through the third aim that individual demographics and the social environment seem to be the predominant determinants of childhood obesity.

BACKGROUND: Transport-related air and noise pollution, exposures linked to adverse health outcomes, varies within cities potentially resulting in exposure inequalities. Relatively little is known regarding inequalities in personal exposure to air pollution or transport-related noise. OBJECTIVES: Our objectives were to quantify socioeconomic and ethnic inequalities in London in 1) air pollution exposure at residence compared to personal exposure; and 2) transport-related noise at residence from different sources. METHODS: We used individual-level data from the London Travel Demand Survey (n = 45,079) between 2006 and 2010. We modeled residential (CMAQ-urban) and personal (London Hybrid Exposure Model) particulate matter £75,000) had lower residential NO2 (-1.3 (95% CI -2.1, -0.6) μg/m3 in the 95th exposure quantile) but higher personal NO2 exposure (1.9 (95% CI 1.6, 2.3) μg/m3 in the 95th quantile), which was driven largely by transport mode and duration. Inequalities in residential exposure to NO2 with respect to area-level deprivation were larger at lower exposure quantiles (e.g. estimate for NO2 5.1 (95% CI 4.6, 5.5) at quantile 0.15 versus 1.9 (95% CI 1.1, 2.6) at quantile 0.95), reflecting low-deprivation, high residential NO2 areas in the city centre. Air pollution exposure at residence consistently overestimated personal exposure; this overestimation varied with age, household income, and area-level income deprivation. Inequalities in road traffic noise were generally small. In logistic regression models, the odds of living within a 50 dB contour of aircraft noise were highest in individuals with the highest household income, white ethnicity, and with the lowest area-level income deprivation. Odds of living within a 50 dB contour of rail noise were 19% (95% CI 3, 37) higher for black compared to white individuals. CONCLUSIONS: Socioeconomic inequalities in air pollution exposure were different for modeled residential versus personal exposure, which has important implications for environmental justice and confounding in epidemiology studies. Exposure misclassification was dependent on several factors related to health, a potential source of bias in epidemiological studies. Quantile regression revealed that socioeconomic and ethnic inequalities in air pollution are often not uniform across the exposure distribution. This work was supported by the UK Natural Environment Research Council, Medical Research Council, Economic and Social Research Council, Department of Environment, Food and Rural Affairs, and Department of Health (NE/I007806/1, NE/I00789X/1, NE/I008039/1) through the cross-research council Environmental Exposures & Health Initiative. The work of the UK Small Area Health Statistics Unit is funded by Public Health England as part of the MRC-PHE Centre for Environment and Health, funded also by the UK Medical Research Council. CT was funded through a Ramón y Cajal fellowship (RYC-2015-17402) awarded by the Spanish Ministry of Economy and Competitiveness