Scan Statistics Adjusted for Global Spatial Autocorrelation

Abstract

Failure to account for global spatial autocorrelation when using scan statistics to find clusters generated by local processes will result in P-values that are too low, and consequently, spurious findings of statistical significance are not uncommon. The presence of global spatial autocorrelation also decreases the ability to reject false null hypotheses and it is therefore more difficult to find local clusters when they exist. By estimating the degree of global autocorrelation and using that estimate to transform the data, it is then possible to apply scan statistics to the transformed data. This results in a reduction in the likelihood of spurious finding of statistical significance when local clusters do not exist.