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A Recursive Algorithm for Spatial Cluster Detection

By Xia Jiang and Gregory F. Cooper

Abstract

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 · 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 2n2 −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 θ(n4). Neill et al.8 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

Topics: Articles
Publisher: American Medical Informatics Association
OAI identifier: oai:pubmedcentral.nih.gov:2655859
Provided by: PubMed Central
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