Spatiotemporal Surveillance Using Biaxially Weighted Scan Statistic

Abstract

______________________________________________________________________Abstract: This paper focuses on signaling the clustering of anomaly with mean shifts. The shift magnitude, location, coverage size, and change point time of clustering are often unknown, which makes the monitoring process challenging. Spatiotemporal scan statistics are effective to detect the existence of clustering. However, none of the existing method is optimal when several parameters about clustering are unspecified. The spatial structure is informative but partially used in the calculation of the scan statistics. This paper proposes a spatiotemporal scan statistic that exponentially weights data according to their time of occurrence and distance to a reference point in space. The results show that the proposed method could be more sensitive than the existing methods depending on the smoothing parameter, size of scan window and other factors. An example of detecting the counties with high incidence of male thyroid cancer is provided to illustrate the effectiveness of the method.

Detecting clusters with increased mean using scan windows with variable radius

“…In each window with a predefined pattern, the EWMA technique was applied to the temporal axis. Lin and Chen [6] applied the weighting technique to both spatial and temporal axes within a given fixed scan radius. The univariate EWMA and cumulative sum (CUSUM) charts are similarly sensitive to detect mean shifts.…”