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

Abstract

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 m2 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.

Unraveling the multi-scalar residential segregation and socio-spatial differentiation in China: A comparative study based on Nanjing and Hangzhou

“…O' Sullivan and Wong (2007) attempted to simulate spatial segregation and scale effect based on kernel density estimation. Fowler (2017) further demonstrated the critical assumption of multi-scalar segregation measurement. Manley (2015) used a multi-level framework to explain the changes of residential differentiation in Auckland, New Zealand at macro, meso and micro scales from 2001 to 2013.…”

Differentiating Black and Hispanic: outcome differences of segregated communities and police shootings in the USA, 2015–2020

“…There are some limitations that should be considered in the interpretation of this analysis. The magnitude of segregation in any given location is the outcome of complex processes taking place at multiple scales (Fowler 2018 ), and our chosen level of census tracts may suffer from an incomplete understanding of the spatial mechanisms of segregation. The dataset on police shootings provides no detail on the race or ethnicity of the police officer, which limits our ability to test the effect of racial or ethnic differences or officer–victim discordance with segregation on the shooting outcome.…”

Country-of-origin-specific economic capital in neighbourhoods: Impact on immigrants’ employment opportunities

“…According to Gru¨nfeld (1970), a neighbourhood thrives best with homogeneity at the level of the direct environment of individuals, but with more diversity at the larger level of the neighbourhood. In this way bonding can occur, but large-scale segregation is countered (see also Fowler, 2018). From social network theory, we know that the probability of finding a job increases with the number of ties (both strong and weak), but also that the distance of a weak tie is important -a weak tie farther away means lower employment probabilities (Granovetter, 1977(Granovetter, , 1983Patacchini and Zenou, 2012).…”