The Analysis of Regional Patterns in Health Data

Three measures of spatial clustering (Moran's I, Geary's c, and a rank adjacency statistic, D) were evaluated for their power to detect regional patterns in health data. The patterns represented various environmental effects: a latitude gradient; residence near a contaminated water supply; disease "hot spots"; relation to socioeconomic status and urbanization; and general spatial autocorrelation. While the methods had high power to detect certain patterns, they were also affected by factors such as the shape of the map, its regional structure, and the spatial distribution of explanatory variables. The power was sometimes low, even for strong geographic trends, particularly for D. Moran's I had the highest power most often. We conclude that use of these methods requires careful specification of the anticipated geographic pattern and awareness of idiosyncratic effects in the study of particular maps.