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Multi-Scale Segregation: Multilevel Modelling of Dissimilarity – Challenging the Stylized Fact that Segregation is Greater the Finer the Spatial Scale

Research output: Contribution to journalArticle

Original languageEnglish
Pages (from-to)566-578
Number of pages18
JournalProfessional Geographer
Volume71
Issue number3
Early online date29 Apr 2019
DOIs
DateAccepted/In press - 30 Oct 2018
DateE-pub ahead of print - 29 Apr 2019
DatePublished (current) - 3 Jul 2019

Abstract

A very large literature has explored the intensity of urban residential segregation using the index of dissimilarity. Several recent studies have undertaken such analyses at multiple spatial scales, invariably reaching the conclusion that the finer-grained the spatial scale the greater the segregation. Such findings over-state the intensity of segregation at finer spatial scales because they fail to take into account an argument made by Duncan et al. (1961) some seventy years ago that indices derived from fine-scale analyses must necessarily incorporate those from coarser scales, with the consequence that finer-scale segregation is invariably over-estimated. Moreover, most studies ignore stochastic variation that results in upward bias in the estimates of segregation. This paper demonstrates the importance of of a recently developed multilevel modelling procedure that identifies the ‘true’ intensity of segregation at every level in a spatial hierarchy net of its intensity at other levels, and net of stochastic variation This is illustrated by both a simulated data set and an empirical study of an English city, with the latter raising important substantive issues regarding the interpretation of segregation patterns and the processes underlying them.

    Research areas

  • segregation, dissimilarity, scale, multilevel modelling

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    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via Taylor & Francis at https://www.tandfonline.com/doi/full/10.1080/00330124.2019.1578977 . Please refer to any applicable terms of use of the publisher.

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