A graph-based multimodal framework to predict gentrification
This work addresses the need for early and targeted policy interventions to protect low-income residents from gentrification, though it is incremental as it builds upon previous machine learning models.
The paper tackles the problem of predicting gentrification in urban areas by proposing a graph-based multimodal deep learning framework that uses urban networks and essential facilities, achieving an average precision of 0.9 in predicting census-tract level gentrification across Chicago, New York City, and Los Angeles.
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.