  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.
