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Issue No. 10 - October (2008 vol. 20)
ISSN: 1041-4347
pp: 1378-1392
Vandana P. Janeja , University of Maryland Baltimore County, Baltimore
Vijayalakshmi Atluri , Rutgers University, Newark
Often, it is required to identify anomalous windows reflecting unusual rate of occurrence of a specific event of interest. Spatial scan statistic approach moves scan window over the region and computes the statistic of a parameter(s) of interest, and identifies anomalous windows. While this approach has been successfully employed, earlier proposals suffer from two limitations: (i) In general, the scan window is regular shaped (e.g., circle, rectangle) identifying anomalous windows of fixed shapes only. However, the region of anomaly is not necessarily regular shaped. Recent proposals to identify windows of irregular shapes identify windows larger than the true anomalies, or penalize large windows. (ii) These techniques account for autocorrelation among spatial data, but not spatial heterogeneity often resulting in inaccurate anomalous windows. We propose a random walk based Free-Form Spatial Scan Statistic (FS3). We construct a Weighted Delaunay Nearest Neighbor graph (WDNN) to capture spatial autocorrelation and heterogeneity. Using random walks we identify natural free-form scan windows, not restricted to a predefined shape and prove that they are not random. FS3 on real datasets has shown that it identifies more refined anomalous windows with better likelihood ratio of it being an anomaly as compared to earlier spatial scan statistic approaches.
Spatial databases, Spatial databases and GIS, anomaly detection, scan statistics

V. P. Janeja and V. Atluri, "Random Walks to Identify Anomalous Free-Form Spatial Scan Windows," in IEEE Transactions on Knowledge & Data Engineering, vol. 20, no. , pp. 1378-1392, 2008.
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