Measuring the scale of segregation using k-nearest neighbor Aggregates

Nearly all segregation measures use some form of administrative unit (usually tracts in the United States) as the base for the calculation of segregation indices, and most of the commonly used measures are aspatial. The spatial measures that have been proposed are often not easily computed, although there have been significant advances in the past decade. We provide a measure that is individually based (either persons or very small administrative units) and a technique for constructing neighborhoods that does not require administrative units. We show that the spatial distribution of different population groups within an urban area can be efficiently analyzed with segregation measures that use population count-based definitions of neighborhood scale. We provide a variant of a k-nearest neighbor approach and a statistic spatial isolation and a methodology (EquiPop) to map, graph, and evaluate the likelihood of individuals meeting other similar race individuals or of meeting individuals of a different ethnicity. The usefulness of this approach is demonstrated in an application of the method to data for Los Angeles and three metropolitan areas in Sweden. This comparative approach is important as we wish to show how the technique can be used across different cultural contexts. The analysis shows how the scale (very small neighborhoods, larger communities, or cities) influences the segregation outcomes. Even if microscale segregation is strong, there may still be much more mixing at macroscales