Spatial cluster detection involves finding spatial subregions of some larger region where clusters of some event are occurring. For example, in the case of disease outbreak detection, we want to find clusters of disease cases so as to pinpoint where the outbreak is occurring. When doing spatial cluster detection, we must first articulate the subregions of the region being analyzed. A simple approach is to represent the entire region by an n x n grid. Then we let every subset of cells in the grid represent a subregion. With this representation, the number of subregions is equal to 2(n2) -1. If n is not small, it is intractable to check every subregion. The time complexity of checking all the subregions that are rectangles is (n(4). Neill et al. performed Bayesian spatial cluster detection by only checking every rectangle. In the current paper, we develop a recursive algorithm which searches a richer set of subregions. We provide results of simulation experiments evaluating the detection power and accuracy of the algorithm.
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