A graph-based multimodal framework to predict gentrification

Javad Eshtiyagh, Baotong Zhang, Yujing Sun, Linhui Wu and Zhao Wang

Papers from arXiv.org

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.

Date: 2023-12, Revised 2023-12
New Economics Papers: this item is included in nep-big and nep-ure
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Published in International Conference on Urban Informatics 2023 - Best Paper Award 3rd Place

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