Prospective spatio-temporal data analysis for security informatics

Wei Chang, Daniel Zeng, Hsinchun Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Scopus citations


Spatio-temporal data analysis plays a central role in many security-related applications including those relevant to transportation infrastructure and border security. In this paper, we investigate prospective spatio-temporal analysis methods that aim to identify "unusual" clusters of events, or hotspots, in both spatial and temporal dimensions. We propose a Support Vector Machine-based approach and compare it with a well-known prospective method based on space-time scan statistic using three problem scenarios. The first two scenarios are based on simulated data with known hotspots. The third scenario uses a real-world crime analysis data set involving vehicles.

Original languageEnglish (US)
Title of host publicationITSC`05
Subtitle of host publication2005 IEEE Intelligent Conference on Transportation Systems, Proceedings
Number of pages5
StatePublished - 2005
Event8th International IEEE Conference on Intelligent Transportation Systems - Vienna, Austria
Duration: Sep 13 2005Sep 16 2005

Publication series

NameIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC


Other8th International IEEE Conference on Intelligent Transportation Systems

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications


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