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A graph-based multimodal framework to predict gentrification

Author

Listed:
  • Javad Eshtiyagh
  • Baotong Zhang
  • Yujing Sun
  • Linhui Wu
  • Zhao Wang

Abstract

Gentrification--the transformation of a low-income urban area caused by the influx of affluent residents--has many revitalizing benefits. However, it also poses extremely concerning challenges to low-income residents. To help policymakers take targeted and early action in protecting low-income residents, researchers have recently proposed several machine learning models to predict gentrification using socioeconomic and image features. Building upon previous studies, we propose a novel graph-based multimodal deep learning framework to predict gentrification based on urban networks of tracts and essential facilities (e.g., schools, hospitals, and subway stations). We train and test the proposed framework using data from Chicago, New York City, and Los Angeles. The model successfully predicts census-tract level gentrification with 0.9 precision on average. Moreover, the framework discovers a previously unexamined strong relationship between schools and gentrification, which provides a basis for further exploration of social factors affecting gentrification.

Suggested Citation

  • 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.
  • Handle: RePEc:arx:papers:2312.15646
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    References listed on IDEAS

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    1. Miguel Padeiro & Ana Louro & Nuno Marques da Costa, 2019. "Transit-oriented development and gentrification: a systematic review," Transport Reviews, Taylor & Francis Journals, vol. 39(6), pages 733-754, November.
    2. Helms, Andrew C., 2003. "Understanding gentrification: an empirical analysis of the determinants of urban housing renovation," Journal of Urban Economics, Elsevier, vol. 54(3), pages 474-498, November.
    3. 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.
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