IDEAS home Printed from https://ideas.repec.org/p/osf/socarx/hkc96_v1.html
   My bibliography  Save this paper

Building a predictive machine learning model of gentrification in Sydney

Author

Listed:
  • Thackway, William
  • Ng, Matthew Kok Ming

    (University of New South Wales)

  • Lee, Chyi Lin
  • Pettit, Christopher

Abstract

In an era of rapid urbanisation and increasing wealth, gentrification is an urban phenomenon impacting many cities around the world. The ability of policymakers and planners to better understand and address gentrification-induced displacement hinges upon proactive intervention strategies. It is in this context that we build a tree-based machine learning (ML) model to predict neighbourhood change in Sydney. Change, in this context, is proxied by the Socioeconomic Index for Advantage and Disadvantage, in addition to census and other ancillary predictors. Our models predict gentrification from 2011-2016 with a balanced accuracy of 74.7%. Additionally, the use of an additive explanation tool enables individual prediction explanations and advanced feature contribution analysis. Using the ML model, we predict future gentrification in Sydney up to 2021. The predictions confirm that gentrification is expanding outwards from the city centre. A spill-over effect is predicted to the south, west and north-west of former gentrifying hotspots. The findings are expected to provide policymakers with a tool to better forecast where likely areas of gentrification will occur. This future insight can then inform suitable policy interventions and responses in planning for more equitable cities outcomes, specifically for vulnerable communities impacted by gentrification and neighbourhood change.

Suggested Citation

  • Thackway, William & Ng, Matthew Kok Ming & Lee, Chyi Lin & Pettit, Christopher, 2021. "Building a predictive machine learning model of gentrification in Sydney," SocArXiv hkc96_v1, Center for Open Science.
    • Handle: RePEc:osf:socarx:hkc96_v1
    DOI: 10.31219/osf.io/hkc96_v1
    as

    Download full text from publisher

    File URL: https://osf.io/download/61bbd10240147e00559a159e/
    Download Restriction: no
    File URL: https://libkey.io/10.31219/osf.io/hkc96_v1?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. Edward L. Glaeser & Hyunjin Kim & Michael Luca, 2019. "Nowcasting the Local Economy: Using Yelp Data to Measure Economic Activity," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 249-273, National Bureau of Economic Research, Inc.
    2. Sue Easton & Loretta Lees & Phil Hubbard & Nicholas Tate, 2020. "Measuring and mapping displacement: The problem of quantification in the battle against gentrification," Urban Studies, Urban Studies Journal Limited, vol. 57(2), pages 286-306, February.
    3. Shigeyuki Hamori & Minami Kawai & Takahiro Kume & Yuji Murakami & Chikara Watanabe, 2018. "Ensemble Learning or Deep Learning? Application to Default Risk Analysis," JRFM, MDPI, vol. 11(1), pages 1-14, March.
    4. 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)

    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. Thackway, William & Ng, Matthew Kok Ming & Lee, Chyi Lin & Pettit, Christopher, 2021. "Building a predictive machine learning model of gentrification in Sydney," SocArXiv hkc96, Center for Open Science.
    2. 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.
    3. 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.
    4. 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.
    5. Olson, Alex, 2020. "Reading the city through its neighbourhoods: Deep text embeddings of Yelp reviews as a basis for determining similarity and change," SocArXiv 8jbvg_v1, Center for Open Science.
    6. Zheng Wang & Jie Shen & Xiang Luo, 2023. "Can residents regain their community relations after resettlement? Insights from Shanghai," Urban Studies, Urban Studies Journal Limited, vol. 60(5), pages 962-980, April.
    7. Dwayne Marshall Baker, 2024. "Burden or benefit: Is retail marijuana facility siting influenced by LULU- or gentrification-related neighbourhood characteristics?," Urban Studies, Urban Studies Journal Limited, vol. 61(6), pages 1049-1070, May.
    8. Na’Taki Osborne Jelks & Viniece Jennings & Alessandro Rigolon, 2021. "Green Gentrification and Health: A Scoping Review," IJERPH, MDPI, vol. 18(3), pages 1-23, January.
    9. Yasser Jezzini & Ghiwa Assaf & Rayan H. Assaad, 2023. "Models and Methods for Quantifying the Environmental, Economic, and Social Benefits and Challenges of Green Infrastructure: A Critical Review," Sustainability, MDPI, vol. 15(9), pages 1-40, May.
    10. Sonja Wilhelm Stanis & Emily Piontek & Shuangyu Xu & Andrew Mallinak & Charles Nilon & Damon M. Hall, 2024. "Residents’ Perceptions of Urban Greenspace in a Shrinking City: Ecosystem Services and Environmental Justice," Land, MDPI, vol. 13(10), pages 1-17, September.
    11. Jonathan Reades, 2020. "Teaching on Jupyter: Using notebooks to accelerate learning and curriculum development," REGION, European Regional Science Association, vol. 7, pages 21-34.
    12. Charles R. Collins & Forrest Stuart & Patrick Janulis, 2022. "Policing gentrification or policing displacement? Testing the relationship between order maintenance policing and neighbourhood change in Los Angeles," Urban Studies, Urban Studies Journal Limited, vol. 59(2), pages 414-433, February.
    13. Joel Alcedo & Alberto Cavallo & Bricklin Dwyer & Prachi Mishra & Antonio Spilimbergo, 2022. "Back to Trend: COVID Effects on E-commerce in 47 Countries," NBER Working Papers 29729, National Bureau of Economic Research, Inc.
    14. Alessandro Rigolon & Timothy Collins, 2023. "The green gentrification cycle," Urban Studies, Urban Studies Journal Limited, vol. 60(4), pages 770-785, March.
    15. Sagi, Alon & Gal, Avigdor & Broitman, Dani & Czamanski, Daniel, 2024. "An unsupervised machine learning approach to the spatial analysis of urban systems through neighbourhoods’ dynamics," Land Use Policy, Elsevier, vol. 144(C).
    16. Ismail, Muhammad & Warsame, Abukar & Wilhelmsson, Mats, 2020. "Measuring Gentrification with Getis-Ord Statistics and Its Effect on Housing Prices in Neighboring Areas: The Case of Stockholm," Working Paper Series 20/19, Royal Institute of Technology, Department of Real Estate and Construction Management & Banking and Finance.
    17. Elliot Anenberg & Chun Kuang & Edward Kung, 2022. "Social learning and local consumption amenities: Evidence from Yelp," Journal of Industrial Economics, Wiley Blackwell, vol. 70(2), pages 294-322, June.
    18. 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).
    19. Cheng-Chien Lai & Wei-Hsin Huang & Betty Chia-Chen Chang & Lee-Ching Hwang, 2021. "Development of Machine Learning Models for Prediction of Smoking Cessation Outcome," IJERPH, MDPI, vol. 18(5), pages 1-10, March.
    20. 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).

    More about this item

    Statistics

    Access and download statistics

    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:osf:socarx:hkc96_v1. 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: OSF (email available below). General contact details of provider: https://arabixiv.org .

    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.