Often, it is required to identify anomalous windows over a spatial region that reflect unusual rate of occurrence of a specific event of interest. A spatial scan statistic-based approach essentially considers a scan window and computes the statistic of a parameter(s) of interest, and identifies anomalous windows by moving the scan window in the region. While this approach has been successfully employed in identifying anomalous windows, earlier proposals adopting spatial scan statistic suffer from two limitations: 1) In general, the scan window should be of a regular shape (e.g., circle, rectangle, cylinder). Thus, most approaches are capable of identifying anomalous windows of fixed shapes only. However, the region of anomaly, in general, is not necessarily of a regular shape. Recent proposals to identify windows of irregular shapes identify windows much larger than the true anomalies or penalize large-sized windows. 2) These techniques take into account autocorrelation among spatial d...