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

TL;DR

This study analyzes social mixing patterns across five global cities using large-scale travel surveys, revealing how mobility, demographics, and transit proximity influence social interactions and exposure.

Contribution

It introduces a comprehensive comparison of social mixing using survey data and develops a graph neural network model to understand mobility's role in social exposure.

Findings

Self-reported survey data shows higher social mixing than neighborhood-based socioeconomic inference.

Older adults over 66 experience greater social mixing, supporting the 'second youth' hypothesis.

Mobility patterns, especially activity spaces, are key to understanding social mixing experiences.

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.