GeoAnalytics Tools Applied to Large Geospatial Datasets

Geovisual analytics focuses on finding location-related patterns and relationship. Many approaches exist but generally do not scale well with large spatial datasets. We propose three enhancements that facilitate scalable geovisual analytics of voluminous geospatial data based on geographic mapping coordinated and linked with parallel coordinates (PC): 1) texture-based geographic mapping that exploits GPU-based rendering performance applied to overview + detail views, 2) statistical methods embedded in PC, 3) aggregated dynamic grid maps that integrate with PC. In this context, we have extended our previous introduced psilaGeoAnalyticspsila Visualization (GAV) framework and class library with a novel implementation of the standard PC using an atomic layered component architecture that allows new ideas to be implemented and assessed without having to rewrite a complete functional PC component. We demonstrate our proposed enhancements applied to a large geospatial dataset containing more than 10,000 Swedish zip (postal) code regions described by more than three million (X, Y) boundary coordinates and includes many associated demographics and statistical attributes.