Geospatial Context Big Table (GeoCBT): Facilitating Geospatial Analysis in Health Research
A consistent finding across health literature is that location-matters. Cancer incidence varies across scales from blocks to neighborhoods to regions. As well, a complex myriad of factors that can affect health outcomes also exists. In rural contexts, aging populations, health care access, sparse populations, environmental exposures, and infrastructure are components. In urban contexts, food-deserts, stress, and pollution (air, water, light, and noise) play possible roles. What is the interaction of all these factors? At what scale(s) is the context and association important? The collection, integration, and use of varied data are foundational to health research. However, time is wasted and effort duplicated by compiling, re-formatting, and integrating the same public sources of information at various geographic levels. In this intervening time, diseases continue to flourish and lives are potentially lost.
Geographic context is an integral component of health research. It is paramount to understand the nature of the ‘environment’ in which individuals are located in order to explore the ways that race, ethnicity, accessibility, contaminants, or other contextual characteristics affect disease incidence and outcomes. This project focuses on benefits of developing a Geospatial Context Big Table (GeoCBT) with 318 million systematic locations (rows), each with a myriad of attributes compiled from public data sources across multiple scales, geographies, and times in a queriable spatial context. There are potentially tens of thousands of attributes (columns) containing functional sociodemographic, environmental, infrastructure, cultural, economic, as well as geospatially derived data (isolation, accessibility, etc.) to provide richer context.
The ability to integrate health research data and information with spatially enabled big data within a common framework has the potential to transform health research. The GeoCBT can catalyze complex health research, broaden geospatial data use and analytics, and enable more cost-effective research.Tim Haith