Research on residential segregation has been active since the 1950s and originated in a desire to quantify the level of racial/ethnic segregation in the United States. The Index of Concentration at the Extremes (ICE), an operationalization of racialized economic segregation that simultaneously captures spatial, racial, and income polarization, has been a popular topic in public health research, with a particular focus on social epidemiology. However, the construction of the ICE metric usually ignores the spatial autocorrelation that may be present in the data, and it is usually presented without indicating its degree of statistical and spatial uncertainty. To address these issues, we propose reformulating the ICE metric using Bayesian modeling methodologies. We use a simulation study to evaluate the performance of each method by considering various segregation scenarios. The application is based on racialized economic segregation in Georgia, and the proposed modeling approach will help determine whether racialized economic segregation has changed over two non-overlapping time points.

翻译：自20世纪50年代以来，居住隔离研究一直活跃，其初衷是量化美国的种族/民族隔离水平。极端集中指数（ICE）作为种族化经济隔离的一种操作化度量，同时捕捉了空间、种族和收入极化，已成为公共卫生研究（尤其聚焦社会流行病学）的热点议题。然而，ICE指标的构建通常忽略数据中可能存在的空间自相关性，且往往未显示其统计不确定性与空间不确定性的程度。为解决这些问题，我们提出利用贝叶斯建模方法重新构建ICE度量。通过模拟研究，我们评估了不同方法在多种隔离情境下的表现。应用基于佐治亚州的种族化经济隔离数据，所提出的建模方法将有助于判断种族化经济隔离在两个非重叠时间点之间是否发生变化。