  Pattern discovery in geo-spatiotemporal data (such as traffic and weather
data) is about finding patterns of collocation, co-occurrence, cascading, or
cause and effect between geospatial entities. Using simplistic definitions of
spatiotemporal neighborhood (a common characteristic of the existing
general-purpose frameworks) is not semantically representative of
geo-spatiotemporal data. We therefore introduce a new geo-spatiotemporal
pattern discovery framework which defines a semantically correct definition of
neighborhood; and then provides two capabilities, one to explore propagation
patterns and the other to explore influential patterns. Propagation patterns
reveal common cascading forms of geospatial entities in a region. Influential
patterns demonstrate the impact of temporally long-term geospatial entities on
their neighborhood. We apply this framework on a large dataset of traffic and
weather data at countrywide scale, collected for the contiguous United States
over two years. Our important findings include the identification of 90 common
propagation patterns of traffic and weather entities (e.g., rain --> accident
--> congestion), which results in identification of four categories of states
within the US; and interesting influential patterns with respect to the
"location", "duration", and "type" of long-term entities (e.g., a major
construction --> more traffic incidents). These patterns and the categorization
of the states provide useful insights on the driving habits and infrastructure
characteristics of different regions in the US, and could be of significant
value for applications such as urban planning and personalized insurance.
