Identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamics
Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China
2 Connection Science, Institute for Data Science and Society, Massachusetts Institute of Technology, Cambridge, MA, United States
3 Grupo Interdisciplinar de Sistemas Complejos (GISC), Department of Mathematics, Universidad Carlos III de Madrid, Leganés, Madrid, Spain
Accepted: 9 May 2023
Published online: 18 May 2023
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.
Key words: Mobility data / Lifestyles / Topic analysis / Non-negative matrix factorization / Segregation / Health Risk / Transportation / Census
© The Author(s) 2023
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