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Understanding urban gentrification through machine learning

Author

Listed:
  • Jonathan Reades

    (King’s College London, UK)

  • Jordan De Souza

    (King’s College London, UK)

  • Phil Hubbard

    (King’s College London, UK)

Abstract

Recent developments in the field of machine learning offer new ways of modelling complex socio-spatial processes, allowing us to make predictions about how and where they might manifest in the future. Drawing on earlier empirical and theoretical attempts to understand gentrification and urban change, this paper shows it is possible to analyse existing patterns and processes of neighbourhood change to identify areas likely to experience change in the future. This is evidenced through an analysis of socio-economic transition in London neighbourhoods (based on 2001 and 2011 Census variables) which is used to predict those areas most likely to demonstrate ‘uplift’ or ‘decline’ by 2021. The paper concludes with a discussion of the implications of such modelling for the understanding of gentrification processes, noting that if qualitative work on gentrification and neighbourhood change is to offer more than a rigorous post-mortem then intensive, qualitative case studies must be confronted with – and complemented by – predictions stemming from other, more extensive approaches. As a demonstration of the capabilities of machine learning, this paper underlines the continuing value of quantitative approaches in understanding complex urban processes such as gentrification.

Suggested Citation

  • Jonathan Reades & Jordan De Souza & Phil Hubbard, 2019. "Understanding urban gentrification through machine learning," Urban Studies, Urban Studies Journal Limited, vol. 56(5), pages 922-942, April.
    • Handle: RePEc:sae:urbstu:v:56:y:2019:i:5:p:922-942
    DOI: 10.1177/0042098018789054
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    References listed on IDEAS

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    Cited by:

    1. Seung-Chul Noh & Jung-Ho Park, 2021. "Café and Restaurant under My Home: Predicting Urban Commercialization through Machine Learning," Sustainability, MDPI, vol. 13(10), pages 1-22, May.
    2. devin michelle bunten & Benjamin Preis & Shifrah Aron-Dine, 2024. "Re-measuring gentrification," Urban Studies, Urban Studies Journal Limited, vol. 61(1), pages 20-39, January.
    3. Jonathan Reades, 2020. "Teaching on Jupyter: Using notebooks to accelerate learning and curriculum development," REGION, European Regional Science Association, vol. 7, pages 21-34.
    4. Karen Chapple & Ate Poorthuis & Matthew Zook & Eva Phillips, 2022. "Monitoring streets through tweets: Using user-generated geographic information to predict gentrification and displacement," Environment and Planning B, , vol. 49(2), pages 704-721, February.
    5. Spandagos, Constantine & Tovar Reaños, Miguel & Lynch, Muireann Á, 2023. "Energy poverty prediction and effective targeting for just transitions with machine learning," Papers WP762, Economic and Social Research Institute (ESRI).
    6. Javad Eshtiyagh & Baotong Zhang & Yujing Sun & Linhui Wu & Zhao Wang, 2023. "A graph-based multimodal framework to predict gentrification," Papers 2312.15646, arXiv.org, revised Dec 2023.
    7. Zhou, You & Zhang, Lingzhu & Chiaradia, Alain J F, 2021. "An adaptation of reference class forecasting for the assessment of large-scale urban planning vision, a SEM-ANN approach to the case of Hong Kong Lantau tomorrow," Land Use Policy, Elsevier, vol. 109(C).
    8. Yuerong Zhang & Karen Chapple & Mengqiu Cao & Adam Dennett & Duncan Smith, 2020. "Visualising urban gentrification and displacement in Greater London," Environment and Planning A, , vol. 52(5), pages 819-824, August.

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