Walter S D
Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada.
Am J Epidemiol. 1992 Sep 15;136(6):742-59. doi: 10.1093/oxfordjournals.aje.a116553.
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.
评估了三种空间聚类方法（莫兰指数I、Geary系数c和一种秩邻接统计量D）检测健康数据中区域模式的能力。这些模式代表了各种环境影响：纬度梯度；居住在受污染水源附近；疾病“热点”；与社会经济地位和城市化的关系；以及一般空间自相关。虽然这些方法在检测某些模式方面具有较高的能力，但它们也受到诸如地图形状、区域结构和解释变量的空间分布等因素的影响。即使对于强烈的地理趋势，检测能力有时也较低，特别是对于统计量D。莫兰指数I最常具有最高的检测能力。我们得出结论，使用这些方法需要仔细确定预期的地理模式，并在研究特定地图时注意特殊影响。
