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A Geospatial Health Context Table for Supporting Public Health Research


Tim Haithcoat






2206A Student Center

This project develops a Big Data table that allows researchers to query across and among multiple data sources integrated by location. The big table created in this way uses location as the fundamental linkage between data sets.  This is the power of geospatial analysis and forms the foundation for the development and interaction with the Health Context Table. The approach utilizes a dense point file populated with attribution derived or obtained directly from public data sources and associated geospatial analysis. The database created extends across the entire continental United States comprising over 300 million points. The data table has at its core, functional socio-demographic data that is pre-processed, cleaned, integrated and represented in its spatial context.  To this core, is being added environmental, infrastructure, cultural, physical, as well as geo-analytically derived layers (i.e. remoteness, isolation).  These data span multiple spatial scales (Census Block Group, Zip Code Tabulation Areas, County, etc.).  The interface to this Big Data table will allow a user to visualize, data mine, analyze uncertainty, and perform data analytics on these data. The Geospatial Health Context Table’s goal is to address the gap in health research and application for an underpinned spatial framework upon which real-world issues and research can be addressed in the context of place. This work is supported by the NIH T32 Training grant (5T32LM012410-02).