Journal
DECISION SUPPORT SYSTEMS
Volume 45, Issue 4, Pages 697-713Publisher
ELSEVIER
DOI: 10.1016/j.dss.2007.12.008
Keywords
Space-time scan; Support vector machine; Algorithm design; Spatio-temporal surveillance method
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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. (C) 2007 Elsevier B.V. All rights reserved.
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