Research Project Details

Measuring Spatial Segregation

Discipline: GIS     Sociology     Urban Studies          

Project Category:
Institution: Stanford University Population Research Institute at The Pennsylvania State University
Principal Investigators: Sean Reardon (Stanford) Stephen A. Matthews (Penn State) Barrett Lee (Penn State) Glenn Firebaugh (Penn State) David O’Sullivan (University of Auckland, New Zealand) Chad Farrell (University of Alaska, Anchorage)
Grant Number: NSF SES-0520400 and SES-0520405

Description: 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. The project will develop and refine a new approach to measuring spatial (race/ethnic) segregation that addresses known flaws in other measures. This 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 training materials (on-line) and opportunities (workshops), and publish descriptive analyses of segregation patterns and trends in order to enable the research community to use these measures. More >>

Expected Outputs: 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 training materials (on-line) and opportunities (workshops), and publish descriptive analyses of segregation patterns and trends in order to enable the research community to use these measures.

Related Publications: Reardon, Sean F. and Glenn Firebaugh. 2002. "Measures of multi-group segregation." Sociological Methodology 32:33-67. Reardon, Sean F. and David O'Sullivan. 2004. "Measures of spatial segregation." Sociological Methodology 34:121-162.

Software Used: VB and ArcGIS 9.x but exploring open source

Contact: Sean F. Reardon (at Stanford) or Stephen A. Matthews (at Penn State)