Identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamics
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...
| Egile Nagusiak: | , , |
|---|---|
| Beste egile batzuk: | |
| Formatua: | Artikulua |
| Hizkuntza: | English |
| Argitaratua: |
Springer Berlin Heidelberg
2023
|
| Sarrera elektronikoa: | https://hdl.handle.net/1721.1/150793 |
| _version_ | 1826195124985528320 |
|---|---|
| author | Yang, Yanni Pentland, Alex Moro, Esteban |
| author2 | MIT Connection Science (Research institute) |
| author_facet | MIT Connection Science (Research institute) Yang, Yanni Pentland, Alex Moro, Esteban |
| author_sort | Yang, Yanni |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T10:07:34Z |
| format | Article |
| id | mit-1721.1/150793 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:07:34Z |
| publishDate | 2023 |
| publisher | Springer Berlin Heidelberg |
| record_format | dspace |
| spelling | mit-1721.1/1507932024-01-08T20:48:58Z Identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamics Yang, Yanni Pentland, Alex Moro, Esteban MIT Connection Science (Research institute) Massachusetts Institute of Technology. Institute for Data, Systems, and Society 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. 2023-05-22T14:13:23Z 2023-05-22T14:13:23Z 2023-05-18 2023-05-21T03:12:20Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/150793 EPJ Data Science. 2023 May 18;12(1):15 PUBLISHER_CC en https://doi.org/10.1140/epjds/s13688-023-00390-w Creative Commons Attribution http://creativecommons.org/licenses/by/4.0/ The Author(s) application/pdf Springer Berlin Heidelberg Springer Berlin Heidelberg |
| spellingShingle | Yang, Yanni Pentland, Alex Moro, Esteban Identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamics |
| title | Identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamics |
| title_full | Identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamics |
| title_fullStr | Identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamics |
| title_full_unstemmed | Identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamics |
| title_short | Identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamics |
| title_sort | identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamics |
| url | https://hdl.handle.net/1721.1/150793 |
| work_keys_str_mv | AT yangyanni identifyinglatentactivitybehaviorsandlifestylesusingmobilitydatatodescribeurbandynamics AT pentlandalex identifyinglatentactivitybehaviorsandlifestylesusingmobilitydatatodescribeurbandynamics AT moroesteban identifyinglatentactivitybehaviorsandlifestylesusingmobilitydatatodescribeurbandynamics |