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.