Spatial scan statistics have been an important class of tools for cluster detection in spatial data. These are often used in support of surveillance and detection activities in public health and other fields. A common limitation of popular spatial scan statistics is the lack of accommodation in the uncertainty of the measure of interest. In a recent JASA Sept. 2009 article, Weighted Normal Spatial Scan Statistic for Heterogeneous Population Data, the authors offer a solution that addresses this problem in more generality. Weights related to local variance measures or proxies such as sample size can be created for use in a weighted likelihood approach. Extensions to non gaussian probability models are addressed. Some case studies and power simulations provided suggest excellent performance. Their solution has been implemented in the freely available software Satscan.
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