This paper explores some visualization methods for characterizing spatial signals detected by SaTScan and discusses how these maps might aid in deciding whether to investigate a signal, as well as the scope and focus of the investigation.

Many heuristics were developed recently to find arbitrarily shaped clusters (see  review  [1]). The most popular statistic is the spatial scan  [2]. Nevertheless, even if all cluster solutions could be known, the problem  of selecting the best cluster is ill posed. This happens because other measures, such as geometric regularity  [3-5] or topology  [6] must be taken intoconsideration. Most cluster finding  methods does not address  this last problem. A genetic multi-objective algorithm was developed elsewhere to identify irregularlyshaped clusters [5].

To evaluate the robustness of a spatial anonymization algorithm for syndromic surveillance data against a triangulation vulnerability attack. `BACKGROUND We have published an anonymization algorithm that takes precise point locations for patients and moves them a randomized distance according to a 2D Gaussian distribution that is inversely adjusted by the underlying population density. Before such algorithms can be integrated into live systems, assurances are needed so that patients cannot be reidentified through systematic vulnerabilities.

Multiple or irregularly shaped spatial clusters are often found in disease or syndromic surveillance maps. We develop a novel method to delineate the contours of spatial clusters, especially when there is not a clearly dominating primary cluster, through artificial neural networks. The method may be applied either for maps divided into regions or point data set maps.