Stratifying and predicting patterns of neighbourhood change and gentrification

An urban analytics approach

Bibliographic Data

ID8962078
AuthorsJoshua Yee (0000-0003-4650-0855, University College London London UK, corresponding author), Adam Dennett (0000-0001-8036-0185, University College London London UK)
Year2021
Volume47
Issue3
Pages770-790
Publication date2021-12-14
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueTransactions of the Institute of British Geographers (JOURNAL)
Journal identifiersISSN: 0020-2754 • E-ISSN: 1475-5661
PublisherWiley (PUBLISHER • GB)
DOI10.1111/tran.12522
OpenAlexW4200607710
LanguageEN
Citations received14
References cited45

While recent debates have widely acknowledged gentrification's varied manifestations, success in enumerating and disentangling the process and its defining features from other forms of neighbourhood change at‐scale and across entire cities, has remained largely elusive. This paper addresses this gap and employs a novel, open and reproducible urban analytics approach to systematically examine the past and future trajectories of neighbourhood change using London, England, as a case‐study example. Using suites of datasets relating to population, house prices, and built environment development, the nature of gentrification's mutations and its spatial patterns are extracted through a multi‐stage data dimensionality reduction and classification methodology. Machine learning is subsequently adopted to model gentrification's observed trends and predict its future frontiers with interactive visualisation methods offering new insights into gentrification's projected dynamics and geographies

Analytics · Data science · Economic geography · Economic growth · Economics · Gentrification · Geography · Neighbourhood (mathematics · Population · Regional science · Sociology · Computer Science · Demography · Housing Market and Economics · Land Use and Ecosystem Services · Mathematics · Urbanization and City Planning

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