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The role of race versus class in shaping residential segregation patterns has been a contentious issue in segregation studies for decades. Despite the voluminous, interdisciplinary literature, scholars have reported conflicting evidence on the role of race versus class in residential segregations. We attribute the current inconclusive literature partially to the failure to consider scale explicitly in residential segregation measures, and partially to the growing complexity of a multiethnic melting pot in most cities in the United States. Inspired by new metrics employed by landscape ecologists to measure landscape heterogeneity, residential segregation is reconceived as a scale-dependent social phenomenon in this paper. We also present an alternative to existing structural or spatial segregation measures, considered as less efficient because most of the existing indices measure only a few dimensions of segregation at a single scale. We have developed a multiscale, lacunarity-based segregation measure, and have used it to examine the role of race versus class in residential segregation patterns in Houston, Texas. Using census-tract-level data from 1980 to 2000, we found that race is still the most important factor in explaining residential segregation despite the overall decline of segregation by both income and race. It was also found that the changing segregation patterns over time are contingent upon the scale as well as the race or income group considered.

It has been recognized that urban residential segregation is a scale-dependent phenomenon, and yet all existing structural or spatial indices measure only certain dimensions of segregation at one single scale. Inspired by new metrics developed by landscape ecologists to measure landscape heterogeneity, in this paper we explore the feasiblity of a multiscale, lacunarity-based segregation measure. We also develop a straightforward GIS-based procedure to calculate this new measure. Our initial simulation results show that lacunarity is an effective measure that can capture multiple dimensions of segregation patterns at multiple scales.