Scalable Spatial Scan Statistics for Trajectories

Author:

Matheny Michael1,Xie Dong1,Phillips Jeff M.1

Affiliation:

1. University of Utah, Utah

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.

Funder

NSF

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Maximizing Range Sum in Trajectory Data;2022 IEEE 38th International Conference on Data Engineering (ICDE);2022-05

2. Statistically-Robust Clustering Techniques for Mapping Spatial Hotspots: A Survey;ACM Computing Surveys;2022-01-18

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