This paper presents the first application of spatially correlated neutral models to the detection of changes in mortality rates across space and time using the local Moran's I statistic. Sequential Gaussian simulation is used to generate realizations of the spatial distribution of mortality rates under increasingly stringent conditions: 1) reproduction of the sample histogram, 2) reproduction of the pattern of spatial autocorrelation modeled from the data, 3) incorporation of regional background obtained by geostatistical smoothing of observed mortality rates, and 4) incorporation of smooth regional background observed at a prior time interval. The simulated neutral models are then processed using two new spatio-temporal variants of the Morany's I statistic, which allow one to identify significant changes in mortality rates above and beyond past spatial patterns. Last, the results are displayed using an original classification of clusters/outliers tailored to the space-time nature of the data. Using this new methodology the space-time distribution of cervix cancer mortality rates recorded over all US State Economic Areas (SEA) is explored for 9 time periods of 5 years each. Incorporation of spatial autocorrelation leads to fewer significant SEA units than obtained under the traditional assumption of spatial independence, confirming earlier claims that Type I errors may increase when tests using the assumption of independence are applied to spatially correlated data. Integration of regional background into the neutral models yields substantially different spatial clusters and outliers, highlighting local patterns which were blurred when local Moran's I was applied under the null hypothesis of constant risk.

中文翻译：

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使用局部Moran's I和地统计学模拟的空间中性模型检测癌症发生率的空间分布的时间变化。

本文介绍了空间相关中性模型在使用局部Moran's I统计量跨时空变化的死亡率检测中的首次应用。在越来越严格的条件下，使用顺序高斯模拟来生成死亡率的空间分布：1）样本直方图的再现； 2）根据数据建模的空间自相关模式的再现； 3）合并通过观察到的死亡率的地统计学平滑处理，以及4）合并在先前时间间隔内观察到的平滑区域背景。然后使用两个新的Morany I统计量的时空变体处理模拟的中性模型，它使人们能够确定过去空间格局之上和之外的死亡率的重大变化。最后，使用根据数据的时空性质定制的聚类/离群值的原始分类显示结果。使用这种新方法，探索了美国所有州经济区（SEA）记录的子宫颈癌死亡率的时空分布，历时9年，每个5年。与传统的空间独立性假设相比，空间自相关的引入导致有效SEA单元的减少，这证实了较早的说法，即当将使用独立性假设的测试应用于空间相关数据时，I类错误可能会增加。将区域背景整合到中性模型中会产生实质上不同的空间簇和离群值，