Scalable Spatial Scan Statistics for Trajectories

Michael Matheny, Dong Xie, Jeff M. Phillips

Research output: Contribution to journalArticlepeer-review

Abstract

We define several new models for how to define anomalous regions among enormous sets of trajectories. These are based on spatial scan statistics, and identify a geometric region which captures a subset of trajectories which are significantly different in a measured characteristic from the background population. The model definition depends on how much a geometric region is contributed to by some overlapping trajectory. This contribution can be the full trajectory, proportional to the length within the spatial region, or dependent on the flux across the boundary of that spatial region. Our methods are based on and significantly extend a recent two-level sampling approach which provides high accuracy at enormous scales of data. We support these new models and algorithms with extensive experiments on millions of trajectories and also theoretical guarantees.

Original languageEnglish (US)
Article number3394046
JournalACM Transactions on Knowledge Discovery from Data
Volume14
Issue number6
DOIs
StatePublished - Oct 2020

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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