Hot Spot Analysis (Getis-Ord Gi*)

Both are local spatial statistics but they detect different patterns. The Gi* statistic used in Hot Spot Analysis only identifies clusters of high values (hot spots) or low values (cold spots). Local Moran's I additionally identifies spatial outliers — features with high values surrounded by low values, and vice versa — making it more informative for detecting anomalies but harder to communicate simply.

A principled approach is to run an Incremental Spatial Autocorrelation analysis: apply Moran's I at increasing distance bands and choose the distance where the global z-score peaks, indicating the spatial scale at which clustering is strongest. An alternative starting point is the mean or median nearest-neighbour distance of the feature set.

Yes. When you test every feature in a large dataset simultaneously at p < 0.05, you expect roughly 5% of features to appear significant by chance. The False Discovery Rate (FDR) correction is widely recommended for Hot Spot Analysis because it balances sensitivity and type I error control better than a strict Bonferroni correction for spatial data.

The Gi* statistic can be applied to count attributes (e.g., number of incidents per polygon), but binary (0/1) data rarely produce reliable results because the distribution assumptions are poorly met. For sparse count data, consider aggregating to a coarser spatial unit or using a different approach such as kernel density estimation.

The general recommendation is a minimum of 30 features, though larger samples (100+) provide more stable z-score distributions. With very few features the normal approximation underlying the z-score is unreliable, and small changes in the data can shift results dramatically.