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

Computer Science > Artificial Intelligence [Submitted on 14 Apr 2026] Latent patterns of urban mixing in mobility analysis across five global cities Z. Fan, B. P. Y. Loo, F. Duarte, C. Ratti, E. Moro 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. Comments: Fan, Z., Loo, B.P.Y., Duarte, F., Ratti, C., & Moro, E. (2026). Latent patterns of urban mixing in mobility analysis across five global cities. Nature Cities, accepted Subjects: Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI) Cite as: arXiv:2604.12202 [cs.AI]   (or arXiv:2604.12202v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.12202 Focus to learn more Submission history From: Becky Loo [view email] [v1] Tue, 14 Apr 2026 02:10:53 UTC (1,200 KB) Access Paper: view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs cs.SI References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)