Quantifying segregation in an integrated urban physical-social space

Area of Science:

• Urban Studies
• Computational Social Science
• Network Science

Background:

• Limited understanding of how cities integrate diverse social classes.
• Existing research primarily focuses on residential segregation by race, neglecting mobility and communication impacts.
• Dynamics of spatial and social-network segregation, and individual variations, remain largely unexplored.

Purpose of the Study:

• To introduce a computational framework for analyzing urban social and spatial segregation.
• To investigate the dynamics of spatio-temporal and social-network segregation using individual-level data.
• To understand the interplay of mobility, social networks, and socio-economic status (SES) in shaping urban integration.

Main Methods:

• Developed a computational framework coupling human mobility, social networks, and SES data.
• Utilized a social similarity measure to analyze segregation dynamics at individual and aggregate levels.
• Applied the framework to large-scale mobile phone and socio-economic datasets in Singapore.

Main Results:

• Wealthier social classes show higher segregation in both physical and social urban spaces.
• Identified 'homophily distance' as a measure of social segregation, influenced by distance decay and homophily.
• Found weak correlations in segregation between physical and social space at the individual level, but strong correlations at aggregated levels.

Conclusions:

• The framework provides a breakthrough in understanding urban segregation dynamics.
• Findings offer insights into the changing landscape of urban segregation and the roles of places over time.
• The methodology can assist social scientists and urban planners in designing more integrated cities.