A statistical method for detecting spatiotemporal co-occurrence patterns
Spatiotemporal co-occurrence patterns (STCOPs) are subsets of Boolean features whose instances frequently co-occur in both space and time. The detection of STCOPs is crucial to the investigation of the spatiotemporal interactions among different features. However, prevalent STCOPs reported
by available methods do not necessarily indicate the statistically significant dependence among different features, which is likely to result in highly erroneous assessments in practice. To improve the reliability of results, this paper develops a statistical method to detect STCOPs and discern
their statistical significance. The proposed method detects STCOPs against the null hypothesis that the spatiotemporal distributions of different features are independent of each other. To construct the null hypothesis, suitable spatiotemporal point-process models considering spatiotemporal
autocorrelation are employed to model the distributions of different features. The performance of the proposed statistical method is assessed by synthetic experiments and a case study aimed at identifying crime patterns among multiple crime types in Portland City. The experimental results
demonstrate that the proposed method is more effective for detecting meaningful STCOPs than the available alternative methods.
Keywords: Spatiotemporal data mining; co-occurrence patterns; crime patterns; point-process model; significance test
Document Type: Research Article
Affiliations: 1: Department of Geo-informatics, Central South University, Changsha, China 2: Faculty of Information Engineering, China University of Geosciences, Wuhan, China
Publication date: 04 May 2019
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