Turnbull's method detects local spatial clusters in group-level data. Populations within the study area are scanned for clusters of cases. A circular window is centered on each region in turn and expanded to include neighboring regions until the total aggregated population within the window equals a user-defined threshold, R. These circular windows may overlap and the counts within the windows will not be independent. This method will be most powerful when the population size at elevated risk is known a priori, otherwise Kulldorff's Spatial Scan is likely to be more robust.
Turnbull et al. (1990) applied this method to examine the distribution of leukemia cases in upstate New York. They called the method the "cluster evaluation permutation procedure." They varied the size of R to see its effect on the analysis. Adjusting their results for multiple comparisons, they found no significant clusters in the upstate New York leukemia data.
