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Pattern discovery in geo-spatiotemporal data (such as traffic and weatherdata) is about finding patterns of collocation, co-occurrence, cascading, orcause and effect between geospatial entities. Using simplistic definitions ofspatiotemporal neighborhood (a common characteristic of the existinggeneral-purpose frameworks) is not semantically representative ofgeo-spatiotemporal data. We therefore introduce a new geo-spatiotemporalpattern discovery framework which defines a semantically correct definition ofneighborhood; and then provides two capabilities, one to explore propagationpatterns and the other to explore influential patterns. Propagation patternsreveal common cascading forms of geospatial entities in a region. Influentialpatterns demonstrate the impact of temporally long-term geospatial entities ontheir neighborhood. We apply this framework on a large dataset of traffic andweather data at countrywide scale, collected for the contiguous United Statesover two years. Our important findings include the identification of 90 commonpropagation patterns of traffic and weather entities (e.g., rain --> accident--> congestion), which results in identification of four categories of stateswithin the US; and interesting influential patterns with respect to the"location", "duration", and "type" of long-term entities (e.g., a majorconstruction --> more traffic incidents). These patterns and the categorizationof the states provide useful insights on the driving habits and infrastructurecharacteristics of different regions in the US, and could be of significantvalue for applications such as urban planning and personalized insurance.

Short and Long-term Pattern Discovery Over Large-Scale Geo-Spatiotemporal Data