A graph-based multimodal framework to predict gentrification

TL;DR

This paper introduces a graph-based multimodal deep learning framework that predicts gentrification at the census-tract level using urban network data, achieving high precision and revealing new social insights.

Contribution

It presents a novel graph-based multimodal deep learning approach for gentrification prediction, integrating urban networks and facilities, and uncovers new social factors influencing gentrification.

Findings

Achieves 0.9 precision in gentrification prediction across three cities.

Discovers a strong relationship between schools and gentrification.

Framework generalizes well to different urban contexts.

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.