Key assumptions in multiscale segregation measures: How zoning and strength of spatial association condition outcomes

Multiscale segregation measures have the potential to increase understanding of residential context and ultimately a wide range of social and spatial processes. By examining segregation at multiple scales, we have the opportunity to study it as more than the outcome of a single process or a measure describing a single contextual effect. Multiscale segregation encourages us to look for sorting processes and contextual effects operating at different scales and potentially even with different meanings. However, the complexity of multiscale measures introduces significant uncertainty about the role of underlying data and assumptions in producing observed outcomes, particularly at fine geographic scales. While traditional measures of segregation have been exposed to decades of scrutiny, multiscale measures are still relatively novel and less well understood. The theoretical contribution of this paper is to consider the implications of segregation as both an outcome and signifier of sorting processes at multiple scales. The empirical contribution is to consider how zoning and the degree of spatial association shape outcomes expressed as multiscale segregation measures. I examine the effects of different allocation strategies for measuring population at small scales by comparing four delineation methods. I find that the method chosen for allocating population to small areas matters, but that by the time observation units reach about 700 m(2) most of the difference between methods has washed out. I also test the effect of changing the degree of assumed spatial association in generating multiscale segregation measures. I find that, as suggested by Reardon and O'Sullivan in their original exposition of their spatial segregation measure, this assumption has a relatively small effect on outcomes and is unlikely to shape substantive findings.