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Abstract:
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We propose a novel scan statistic algorithm for finding spatial clusters. Instead of specifying locations for the possible clusters a priori, as is currently done for cluster finders based on focused algorithms, we alter the usual adjacency induced by the common geographical boundary between regions, according to certain environmental features of interest associated with the map. We build various plausible scenarios, each time modifying the adjacency structure on specific geographic areas in the map, and run a multi-objective genetic algorithm to find irregularly shaped clusters. Therefore the probability of cluster detection is increased or decreased, according to the changes made in the adjacency graph structure, providing a tool for testing hypotheses concerning the adequacy of environmentally defined models for clustering of diseases.
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