Comparing spatial clustering tests based on rare to common spatial events

This paper compares the performance of three clustering tests––Rogerson R, Getis-Ord G and Lin-Zeng LR-T––using a range of simulated sample distributions from rare to common spatial events. It is shown that all of the tests are sensitive to high value clustering, and all but G are sensitive to low-value clustering. For a spatial pattern exhibiting negative spatial autocorrelation, R is likely to associate the autocorrelation with clustering when sample size is greater than 20, while LR-T and G are unlikely to accept any presence of negative autocorrelation as clustering.