A graph-based multimodal framework to predict gentrification
- URL: http://arxiv.org/abs/2312.15646v2
- Date: Wed, 27 Dec 2023 16:37:10 GMT
- Title: A graph-based multimodal framework to predict gentrification
- Authors: Javad Eshtiyagh, Baotong Zhang, Yujing Sun, Linhui Wu, Zhao Wang
- Abstract summary: We propose a novel graph-based multimodal deep learning framework to predict gentrification based on urban networks of tracts and essential facilities.
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.
- Score: 4.429604861456339
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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