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.

Gentrification is a social process where wealthier households move into a neighborhood. It increases the average household income, encourages the emergence of new businesses, and causes demographic displacement. The reasons behind the troublesome gentrification of Chicago go back to the 1920s. At that time, gentrification was partly encouraged by stimulating private investment and paving the way for urban renewal operations. In the 1950s, many white families moved out of the urban neighborhoods while the Latinos, African Americans, and other minorities moved into the city.