Highly similar but unassociated results often occur when both hot spot results are dominated by a single category, such as not significant, or when both results have large clusters of features with the same significance level.

This means that despite the similarity of the significance levels, attempts to influence one variable (such as mitigation efforts) will not produce changes in the other variable. The distinction between similarity and association is important because it is common for two hot spot results to be highly similar (many corresponding features and their neighbors have the same significance level) but still have little association or dependence. The association (or dependence) measures the strength of the underlying statistical relationship between the hot spot variables (similar to correlation for continuous variables). The similarity measures how closely the hot spots, cold spots, and nonsignificant areas of both hot spot results spatially align.

All comparisons are performed by comparing the significance level categories (99% hot, 95% hot, 90% hot, not significant, 90% cold, 95% cold, and 99% cold) between corresponding features and their neighbors in both input layers.

For windows instal Hotspot Maker 3.1