Urban Social Prediction

This project develops predictive models and decision-support tools for anticipating the social and economic impacts of urban renewal.

About

Urban Social Prediction develops machine-learning models and decision-support tools for anticipating the social and economic impacts of urban renewal. The project helps municipalities and planners explore how redevelopment may affect communities before decisions become difficult to reverse.

Urban growth creates pressure on housing, infrastructure, services, mobility, and social resilience. Renewal projects can bring investment and improved living conditions, but they can also produce displacement, uneven benefits, and unexpected local consequences. This project uses data-driven methods to make those risks and opportunities more visible during planning.

Our work combines predictive modeling, spatial analysis, and interpretable planning interfaces. We study which factors shape renewal outcomes, how models can be validated across different urban contexts, and how planning teams can use predictions without treating them as automatic decisions.

The project also develops recommendation tools for strategic urban renewal. These tools help stakeholders compare parcel combinations, feasibility constraints, redevelopment potential, and prioritization criteria. The aim is to support evidence-based decisions while keeping social equity, transparency, and local context at the center of the process.