Exploring Economic Sectoral Dynamics Through High-resolution Mobility Data
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908503078076416 |
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| author | Leslie, Timothy F Amiri, Hossein Züfle, Andreas |
| author_facet | Leslie, Timothy F Amiri, Hossein Züfle, Andreas |
| contents | We present a comprehensive dataset capturing patterns of human mobility across the United States from January 2019 to January 2023, based on anonymized mobile device data. Aggregated weekly, the dataset reports visits, travel distances, and time spent at public locations organized by economic sector for approximately 12 million Points of Interest (POIs). This resource enables the study of how mobility and economic activity changed over time, particularly during major events such as the COVID-19 pandemic. By disaggregating patterns across different types of businesses, it provides valuable insights for researchers in economics, urban studies, and public health. To protect privacy, all data have been aggregated and anonymized. This dataset offers an opportunity to explore the dynamics of human behavior across sectors over an extended time period, supporting studies of mobility, resilience, and recovery. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_13985 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Exploring Economic Sectoral Dynamics Through High-resolution Mobility Data Leslie, Timothy F Amiri, Hossein Züfle, Andreas Computers and Society We present a comprehensive dataset capturing patterns of human mobility across the United States from January 2019 to January 2023, based on anonymized mobile device data. Aggregated weekly, the dataset reports visits, travel distances, and time spent at public locations organized by economic sector for approximately 12 million Points of Interest (POIs). This resource enables the study of how mobility and economic activity changed over time, particularly during major events such as the COVID-19 pandemic. By disaggregating patterns across different types of businesses, it provides valuable insights for researchers in economics, urban studies, and public health. To protect privacy, all data have been aggregated and anonymized. This dataset offers an opportunity to explore the dynamics of human behavior across sectors over an extended time period, supporting studies of mobility, resilience, and recovery. |
| title | Exploring Economic Sectoral Dynamics Through High-resolution Mobility Data |
| topic | Computers and Society |
| url | https://arxiv.org/abs/2506.13985 |