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In attempting to analyze geographic clusters of disease, one is obliged to take account of varying population density.Customary methods, for example calculation of rates for subregions, are unsatisfactory: if subregions are too large, geographic detail is lost; if too small, stable rates cannot be calculated.In either case, adjacency relationships among cases are not easily analyzed.The usual difficulties are avoided by using an innovative projection technique.On a transformed map, population density is equalized while preserving adjacency relationships, so that clusters of cases can be easily recognized and their statistical significance measured.In this paper a density-equalizing computer algorithm is briefly described, and its use illustrated in representative applications.
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