The use of open source GIS algorithms, big geographic data, and cluster computing techniques to compile a geospatial database that can be used to evaluate upstream bathing and sanitation behaviours on downstream health outcomes in Indonesia, 2000-2008.

Advances in big-data availability, particularly high-resolution elevation data, the lowering of the cost of parallel computing options, mass survey data, and open source GIS algorithms that can utilize parallel processing and big-data, open new opportunities for the study of human health at micro granularities but across entire nations. The database generated has already been used by health researchers to compute the influence of upstream behaviors on downstream diarrhea outbreaks and to monitor avoidance behaviors to upstream water behaviors across all downstream 250,000 Indonesian villages over 4 years, and further waterborne health analyses are underway.