Modifications to Spatial Scan Statistics for Estimated Probabilities at Fine- Resolution in Highly Skewed Spatial Distributions

OBJECTIVE Modifications to spatial scan statistics are investigated for prospective cluster detection at fine-resolution with highly skewed spatial distributions having many spatial zones with very few cases. Several alternative methods for the estimation of spatial probabilities and expected counts from counts in a baseline data window are evaluated with the Poisson spatial scan statistic (1) and the space-time permutation scan statistic (2) using goodness-of-fit statistics and cluster rates to compare performance. BACKGROUND Estimation of representative spatial probabilities and expected counts from baseline data can cause problems in applying spatial scan statistics when observed events are sparse in a large percentage of the spatial zones (e.g., zip codes or census tracts) found in the data re- cords. In applications of scan statistics to datasets with