Discovering SpatioTemporally Invariant Event Patterns From Mobility Data

The discovery of sequential patterns from spatiotemporal data is known to be a very complex data mining task. The relevance of spatiotemporal patterns to study event correlations in mobility data is established. Prior works addressed either the separate analysis of spatial and temporal dependencies among data, such as the co-location of events, or the study of the joint spatiotemporal properties of the trajectories observed over a region of interest. The aim of this paper is instead to overcome existing approaches by extracting sequences of discrete events showing spatiotemporally invariant properties. For example, if an arbitrary bike sharing station becomes full (all its docks are used) then we will observe an increase in the occupancy level of the bike sharing stations in the surrounding area within ten minutes. We denote such a new pattern as a SpatioTemporally Invariant (STInv) event pattern because we observe several instances in the source data differing just in spatiotemporal shifts. We also propose a new algorithm to mine STInvs based on a prefix-projected sequential pattern growth approach and different quality metrics to quantify the contribution of the spatial invariance. The proposed approach is empirically evaluated on two mobility datasets related to a bike sharing system and traffic data. The results confirm the usability of the proposed solution in real-world scenarios.