Spatio-temporal co-occurring patterns represent subsets of event types that occur together in both space and time. In comparison to previous work in this field, we present a general framework to identify spatio-temporal cooccurring patterns for continuously evolving spatio-temporal events that have polygon-like representations. We also propose a set of measures to identify spatio-temporal co-occurring patterns and propose an Apriori-based spatio-temporal cooccurrence mining algorithm to find prevalent spatio-temporal co-occurring patterns for extended spatial representations that evolve over time. We evaluate our framework on real-life data to demonstrate the effectiveness of our measures and the algorithm. We present results highlighting the importance of our measures in identifying spatio-temporal co-occurrence patterns.

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Spatiotemporal co-occurrence patterns (STCOPs) represent the subsets of event types that occur together in both space and time. However, the discovery of STCOPs in data sets with extended spatial representations that evolve over time is computationally expensive because of the necessity to calculate interest measures to assess the co-occurrence strength, and the number of candidates for STCOPs growing exponentially with the number of spatiotemporal event types. In this paper, we introduce a novel and effective filterand-refine algorithm to efficiently find prevalent STCOPs in massive spatiotemporal data repositories with polygon shapes that move and evolve over time. We provide theoretical analysis of our approach, and follow this investigation with a practical evaluation of our algorithm effectiveness on three real-life data sets and one artificial data set.

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