The study of the causes, patterns, and consequences of racial and socioeconomic residential segregation requires the careful measurement of segregation patterns. This, in turn, requires that measures of segregation incorporate an understanding of spatial proximity/distance, something that is now possible due to the increasing availability, sophistication, and ease-of-use of desktop geographical information system (GIS) software. This project will develop and refine a new approach to measuring spatial (race/ethnic) segregation that addresses known flaws in other measures. The approach is based on the understanding that a segregation index is a measure of the extent to which the local environments of individuals differ in their racial or socioeconomic composition (or, more generally, on any population trait). This approach is operationalized by assuming each individual inhabits a 'local environment' whose population is made up of the spatially-weighted average of the populations at each point in the region of interest. Given a particular spatial weighting function, segregation is measured by computing the spatially-weighted racial (or socioeconomic) composition of the local environment of each location (or person) in the study region and then comparing the average compositions of the local environments of members of each group. This approach has a number of features that make it well suited to measuring spatial segregation. In particular, measures derived from this approach 1) are independent of choices of tract boundaries; 2) are sensitive to segregation patterns at any scale; 3) measure both spatial exposure and spatial evenness; 4) can be computed using any theory-based definition of spatial proximity and distance; 5) measure segregation among multiple racial/ethnic groups; and 6) are readily adaptable to the measurement of income segregation. This project will develop, evaluate, and refine a set of measures of segregation that a) are computable from available census and geospatial data, and b) enable researchers to measure segregation based on theory-driven definitions of social proximity and distance. In addition, the project will develop software tools, provide on-line training materials, conduct workshops, and publish descriptive analyses of segregation patterns and trends in order to enable the research community to use these measures.
Residential segregation by race and income remains a stubborn feature of U.S. society, and a growing body of scholarship shows that segregation is associated with negative outcomes for families, youth, and children living in isolated poor and minority neighborhoods. Income segregation, which results in the concentration of poverty, appears to have particularly negative effects for children in disadvantaged neighborhoods, including lower rates of high school completion and higher rates of teen pregnancy. Consequently, the study of racial and socioeconomic residential segregation is an important area of scholarship with significant implications for social policy. This project will produce a) technical knowledge regarding the measurement of segregation; b) user-friendly software tools and training materials to enable other researchers to use the newly-developed methods of measuring segregation; and c) detailed descriptive data on patterns and trends of racial and socioeconomic segregation in U.S. metropolitan areas. These tools and descriptive results will enable researchers to better understand the causes, patterns, and consequences of residential segregation.
