Identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamics
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13688_2023_Article_390.pdf
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Author(s)
Yang, Yanni
Pentland, Alex
Moro, Esteban
Date Issued
May 18, 2023
Publisher
Springer Berlin Heidelberg
Citation
EPJ Data Science. 2023 May 18;12(1):15
Version
Final published version
Abstract
Abstract
Urbanization and its problems require an in-depth and comprehensive understanding of urban dynamics, especially the complex and diversified lifestyles in modern cities. Digitally acquired data can accurately capture complex human activity, but it lacks the interpretability of demographic data. In this paper, we study a privacy-enhanced dataset of the mobility visitation patterns of 1.2 million people to 1.1 million places in 11 metro areas in the U.S. to detect the latent mobility behaviors and lifestyles in the largest American cities. Despite the considerable complexity of mobility visitations, we found that lifestyles can be automatically decomposed into only 12 latent interpretable activity behaviors on how people combine shopping, eating, working, or using their free time. Rather than describing individuals with a single lifestyle, we find that city dwellers’ behavior is a mixture of those behaviors. Those detected latent activity behaviors are equally present across cities and cannot be fully explained by main demographic features. Finally, we find those latent behaviors are associated with dynamics like experienced income segregation, transportation, or healthy behaviors in cities, even after controlling for demographic features. Our results signal the importance of complementing traditional census data with activity behaviors to understand urban dynamics.
MIT Department
MIT Connection Science (Research institute)
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Terms of Use
Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1140/epjds/s13688-023-00390-w
