Neighborhood Gentrification

Neighborhood gentrification, the transformation of low-income areas by influxes of wealthier residents, is a complex process studied to understand its impacts and predict its occurrence. Current research employs machine learning models, including graph-based deep learning and graph autoencoders, to analyze socioeconomic data, urban networks, and even street-level imagery to identify gentrification patterns and predict its likelihood with improved accuracy. These predictive models aim to assist policymakers in mitigating negative consequences for existing residents while leveraging the potential positive aspects of neighborhood revitalization. The integration of diverse data sources and advanced algorithms offers a more comprehensive understanding of gentrification's multifaceted dynamics and its effects on urban communities.