A stack-based prospective spatio-temporal data analysis approach

Authors:
Wei Chang;Daniel Zeng;Hsinchun Chen
Affiliations:
Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA 15260, USA;Department of Management Information Systems, University of Arizona, Tucson, AZ 85721, USA;Department of Management Information Systems, University of Arizona, Tucson, AZ 85721, USA
Venue:
Decision Support Systems
Year:
2008

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Abstract

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.