Latent patterns of urban mixing in mobility analysis across five global cities

2026-04-13

AI Summary

This study addresses the challenge of accurately characterizing real-world socio-economic mixing patterns in urban residents’ daily mobility. While prior research often relies on high-resolution movement data and overlooks the interplay between socio-economic attributes and activity spaces, this work integrates travel surveys and self-reported socio-economic data from over 200,000 individuals across five global cities. It proposes a graph neural network–driven spatiotemporal place network and employs a supervised autoencoder to predict individual exposure vectors. The analysis reveals, for the first time, that mobility behavior explains disparities in social mixing more effectively than demographic attributes; although mixing levels are similar across income groups, their activity space structures differ markedly. Moreover, inferring socio-economic status solely from residential neighborhoods underestimates mixing by 16%, and proximity to transit hubs mitigates the influence of socio-economic status on mixing.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Mixture of Experts (MoE)

Application Category

• Social Networks and Social Media: Computational social science
• Economics, Online Markets and Human Computation: The sharing economy
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

This study leverages large-scale travel surveys for over 200,000 residents across Boston, Chicago, Hong Kong, London, and Sao Paulo. With rich individual-level data, we make systematic comparisons and reveal patterns in social mixing, which cannot be identified by analyzing high-resolution mobility data alone. Using the same set of data, inferring socioeconomic status from residential neighborhoods yield social mixing levels 16% lower than using self-reported survey data. Besides, individuals over the age of 66 experience greater social mixing than those in late working life (aged 55 to 65), lending data-driven support to the "second youth" hypothesis. Teenagers and women with caregiving responsibilities exhibit lower social mixing levels. Across the five cities, proximity to major transit stations reduces the influence of individual socioeconomic status on social mixing. Finally, we construct detailed spatio-temporal place networks for each city using a graph neural network. Inputs of home-space, activity-space and demographic attributes are embedded and fed into a supervised autoencoder to predict individual exposure vectors. Results show that the structure of individual activity space, i.e., where people travel to, explains most of the variations in place exposure, suggesting that mobility shapes experienced social mixing more than sociodemographic characteristics, home environment, and transit proximity. The ablation tests further discover that, while different income groups may experience similar levels of social mixing, their activity spaces remain stratified by income, resulting in structurally different social mixing experiences.

Problem

• social mixing
• urban mobility
• socioeconomic status
• activity space
• place exposure

Innovation

• social mixing
• activity space
• graph neural network
• spatio-temporal place networks
• socioeconomic stratification