Improving assessment of urban racial segregation by partitioning a region into racial enclaves

Abstract

Frequently, a single-value metric is needed to rank urban regions with respect to the level of multiracial segregation or to compare a segregation level of a single urban region at two different times. Assessment of segregation depends not only on a metric used but also on a choice of region’s partitioning. The standard practice is to partition the region into single-scale subregions. In the United States, census tracts are the subregions of choice. Census aggregation units including tracts are delineated without a direct regard to racial homogeneity and are in fact heterogeneous. Consequently, using tracts as subdivisions leads to the underestimation of the segregation level of the entire region. Here we propose to partition a region into racial enclaves—units having boundaries that align with transitions between different racial compositions. By reflecting true demographic structure, such units minimize their internal racial inhomogeneity resulting in improved assessment of segregation. Enclaves are defined as aggregates of adjacent census blocks (smallest and the most racially homogeneous census units) of similar composition. In a typical US urban region, effective population size of enclaves is an order of magnitude larger than the size of a census tract and yet the segregation calculated based on enclaves is larger than segregation based on census tracts. The proposed methodology is described and applied to a set of 61 largest cities in the US in their metropolitan statistical areas as well as their urban areas boundaries using 1990 and 2010 block-level data. The method is compared to the standard methodology using correlations between cities’ segregation rankings.