Trajectories of neighborhood change

Doctoral Thesis (2018)
Author(s)

MD Zwiers (TU Delft - OLD Urban Renewal and Housing)

Contributor(s)

M Ham – Promotor (TU Delft - OLD Urban Renewal and Housing)

R.J. Kleinhans – Promotor (TU Delft - OLD Urban Renewal and Housing)

David Manley – Copromotor (TU Delft - OLD Urban Renewal and Housing)

Research Group
OLD Urban Renewal and Housing
Copyright
© 2018 M.D. Zwiers
DOI related publication
https://doi.org/10.7480/abe.2018.21
More Info
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Publication Year
2018
Language
English
Copyright
© 2018 M.D. Zwiers
Research Group
OLD Urban Renewal and Housing
ISBN (print)
978-94-6366-068-6
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

Neighborhoods represent a scale at which inequalities are reflected in the unequal spatial distribution of ethnic and income groups across urban space. However, neighborhoods are not static entities and spatial patterns of socioeconomic and ethnic inequality shift over time as a result of processes of neighborhood change. This dissertation has adopted a longitudinal approach to analyze patterns of neighborhood change on a relatively low spatial scale. This dissertation illustrates that neighborhoods remain relatively stable over time in their socioeconomic and ethnic status and that change takes several decades to take effect. This dissertation finds that neighborhoods exhibit a strong degree of path-dependency and demonstrates how the housing stock influences neighborhood trajectories. In addition, it shows how large-scale changes to the housing stock in the context of urban restructuring affect residential mobility and neighborhood upgrading. This dissertation also reveals the ways in which different population dynamics interact to inhibit or generate neighborhood change to reproduce socio-spatial inequalities. Moreover, the innovative methods that are explored in this dissertation contribute to broadening the scope of statistical methods for the longitudinal analysis of neighborhood change.

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