Spatial segregation, segregation indices and the geographical perspective

Abstract

What could be more inherently geographical than segregation? However, the richness of the spatial variations in segregation is seldom captured by the dominant genre of empirical research. Returning the ‘geography’ to segregation research, we argue that local areas need to be given considerably more attention, using measures that explicitly reveal the spatial fabric of residential clustering along racial/ethnic lines. We first critique global measures such as the Dissimilarity Index and its spatial counterparts. Attention then turns to local measures such as the Location Quotient and Local Moran's I, applying them to Franklin County, Ohio, the core of Columbus MSA (Metropolitan Statistical Area). Our interpretation of the findings also employs local knowledge concerning neighbourhood characteristics, ongoing urban processes, historical occurrences, and the like. Thus, while local indices based on secondary data expose the terrain of clustering/segregation, follow‐up fieldwork and/or secondary data analysis in a mixed‐methods framework provides a better understanding of the ground‐level reality of clustering/segregation. Tangible evidence of the gain from this approach is provided by our evaluation of conventional frameworks for understanding racial/ethnic aspects of residential patterning – assimilation, stratification and resurgent ethnicity – and in our proposal for a new framework, ‘market‐led pluralism’, which focuses on market makers who represent the supply side of housing. Copyright © 2006 John Wiley & Sons, Ltd.

Cultural diversity: a matter of measurement

“…It does not take account of the location of group clusters, a phenomenon known as the checkerboard problem (Brown and Chung, 2006). The checkerboard problem recognizes that there may be one big cluster of a group, or many small communities scattered around the total area, but no way of knowing which one is present from a global index calculation.…”

Progress in Spatial Demography

“…Applications of ESDA and of local statistical analysis to race/ethnic diversity and segregation can be found in Frank (2003), Brown, and Chung (2006); and in non-U.S. examples, such as in Borruso (2009) and in Johnston, Poulson, and Forrest (2010). Frank (2003) and Brown and Chung (2006) employed spatial autocorrelation and ESDA methods to describe and compare socioeconomic and racial residential patterns using global and local spatial statistics. Johnston and colleagues (2011) offered an innovative method for classifying -ethnoburbs,‖ or wealthy sub-units of ethnic minorities nested within larger suburbs in New Zealand.…”

School segregation in Jefferson County and the affiliated Louisville metropolitan area, USA

“…However, critics argue that classic segregation measures such as the dissimilarity index are aspatial because they merely capture a statistical summarization of racial/ethnic disparities in a study area but fail to take the spatial aspects of different demographic groups into consideration, thereby neglecting the geographic dimensions of segregation (Brown & Chung, 2006;Gilliland & Olson, 2010;Kaplan & Douzet, 2011). Complementing the classic segregation measure, GIS-oriented segregation approaches like the S index, an ellipse-based segregation indicator that estimates the extent to which two or more demographic groups are geographically separated, accounts for both the statistical characteristics and the spatial patterns of different racial/ ethnic groups within a study area (Wong, 1999).…”