Using urban big data to redefine experienced social segregation: how it is driven by mobility, built environment, and residence

Sustainable urbanisation features diverse populations and social cohesion. However, cities nowadays face deepening social segregation.

A better understanding of segregation through innovative perspectives and data is timely and crucial for mitigating social segregation and inequalities.

The study of social segregation has mainly focused on residential segregation or the potential opportunities for interactions between groups.

Experienced social segregation is determined by the actual interactions between people, which were challenging to capture until big human mobility data became globally available.

These mobility data are increasingly used to describe experienced social segregation, but the limited literature rarely explains segregation in a comprehensive way.

This project aims to (1) use urban big data to reinvent the concept of experienced social segregation and (2) explore how social segregation is explained by mobility behaviours, the built environment, and residence in global regions, by leveraging advanced techniques in computational social science and mobility systems.

This project will provide profound insights into how experienced social segregation distributes across different regions.

Moreover, the explanations of observed social segregation can be used to make effective place-based policies and urban planning that go beyond the housing boundaries, address inequality issues of race, income, etc., and promote diversity to boost innovation.