A stack-based prospective spatio-temporal data analysis approach

Wei Chang, Daniel Zeng, Hsinchun Chen

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


Spatio-temporal data analysis has recently gained considerable attention from both the research and practitioner communities because of the increasing availability of datasets with prominent spatial and temporal data elements. In this paper, we develop a new spatio-temporal data analysis approach aimed at discovering abnormal spatio-temporal clustering patterns. We also propose a quantitative evaluation framework and compare our approach against a widely used space-time scan statistic-based method under this framework. Our approach is based on a robust clustering engine using support vector machines and incorporates ideas from existing online surveillance methods to track incremental changes over time. Initial experimental results using both simulated and real-world datasets indicate that our approach is able to detect abnormal areas with irregular shapes more accurately than the space-time scan statistic-based method.

Original languageEnglish (US)
Pages (from-to)697-713
Number of pages17
JournalDecision Support Systems
Issue number4
StatePublished - Nov 2008


  • Algorithm design
  • Space-time scan
  • Spatio-temporal surveillance method
  • Support vector machine

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Information Systems and Management


Dive into the research topics of 'A stack-based prospective spatio-temporal data analysis approach'. Together they form a unique fingerprint.

Cite this