IDEAS home Printed from https://ideas.repec.org/a/sae/envira/v52y2020i5p819-824.html
   My bibliography  Save this article

Visualising urban gentrification and displacement in Greater London

Author

Listed:
  • Yuerong Zhang

    (Bartlett School of Planning, University College London, UK; Centre for Advanced Spatial Analysis, University College London, UK)

  • Karen Chapple

    (College of Environmental Design, University of California, Berkeley, USA)

  • Mengqiu Cao

    (Bartlett School of Planning, University College London, UK; School of Architecture and Cities, University of Westminster, UK)

  • Adam Dennett

    (Centre for Advanced Spatial Analysis, University College London, UK)

  • Duncan Smith

Abstract

Gentrification has long been a contentious issue which has prompted debate among scholars due to variations in its location, timing, context and types of measurements used. Therefore, it is worth seeking a simple and effective approach to measure the processes of gentrification, which enables comparative studies to be conducted across different cities around the world. Using six sets of thematic data from 2001 and 2011 at the neighbourhood level, this study proposes five types of gentrification and displacement by using Chapple and Zuk’s theoretical framework. London was selected as a case study. The results show that gentrification was sweeping in many ways during the 2000s in London, particularly in Inner East London. Some areas in North West London are identified as vulnerable neighbourhoods at risk of displacement and gentrification. Furthermore, it was found that most of the neighbourhoods experiencing ongoing displacement are concentrated in Outer London and Inner South London. The typology provides a useful starting point for planners and policymakers to gain deeper insights into the progress of gentrification in London. Additionally, this work can serve as an example to illustrate the potential for using similar types of open source code and census data to estimate the degree of gentrification in other cities.

Suggested Citation

  • 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.
  • Handle: RePEc:sae:envira:v:52:y:2020:i:5:p:819-824
    DOI: 10.1177/0308518X19880211
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0308518X19880211
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0308518X19880211?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mikio Yoshida & Haruka Kato, 2023. "Housing Affordability Risk and Tourism Gentrification in Kyoto City," Sustainability, MDPI, vol. 16(1), pages 1-14, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jonathan Reades, 2020. "Teaching on Jupyter: Using notebooks to accelerate learning and curriculum development," REGION, European Regional Science Association, vol. 7, pages 21-34.
    2. 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).
    3. 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).
    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. 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.
    6. 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.
    7. 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.
    8. Pengyuan Liu & Yan Zhang & Filip Biljecki, 2024. "Explainable spatially explicit geospatial artificial intelligence in urban analytics," Environment and Planning B, , vol. 51(5), pages 1104-1123, June.
    9. Spandagos, Constantine & Tovar Reaños, Miguel Angel & Lynch, Muireann Á., 2023. "Energy poverty prediction and effective targeting for just transitions with machine learning," Energy Economics, Elsevier, vol. 128(C).
    10. Jan Voltaire Vergara & Maria Y Rodriguez & Jonathan Phillips & Ehren Dohler & Melissa L Villodas & Amy Blank Wilson & Kenneth Joseph, 2024. "An evaluation framework for predictive models of neighbourhood change with applications to predicting residential sales in Buffalo, NY," Urban Studies, Urban Studies Journal Limited, vol. 61(5), pages 838-858, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:envira:v:52:y:2020:i:5:p:819-824. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.