Estimating Hourly Neighborhood Population Using Mobile Phone Data in the United States
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866908521711271936 |
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| author | Ning, Huan Li, Zhenlong Yu, Manzhu Zhang, Shiyan Qiao, Shan |
| author_facet | Ning, Huan Li, Zhenlong Yu, Manzhu Zhang, Shiyan Qiao, Shan |
| contents | Traditional population estimation techniques often fail to capture the dynamic fluctuations inherent in urban and rural population movements. Recognizing the need for a high spatiotemporal dynamic population dataset, we propose a method using smartphone-based human mobility data to reconstruct the hourly population for each neighborhood across the US. We quantify population fluctuations on an hourly, diurnal, daily, and seasonal basis, and compare these with static population data to highlight the limitations of traditional models in capturing temporal dynamics. This study is one of the first hourly population products at a large geographic extent (US), contributing to various studies that involve dynamic populations with high spatiotemporal resolution, such as air pollution exposure analysis and emergency response. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_01170 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Estimating Hourly Neighborhood Population Using Mobile Phone Data in the United States Ning, Huan Li, Zhenlong Yu, Manzhu Zhang, Shiyan Qiao, Shan Social and Information Networks Traditional population estimation techniques often fail to capture the dynamic fluctuations inherent in urban and rural population movements. Recognizing the need for a high spatiotemporal dynamic population dataset, we propose a method using smartphone-based human mobility data to reconstruct the hourly population for each neighborhood across the US. We quantify population fluctuations on an hourly, diurnal, daily, and seasonal basis, and compare these with static population data to highlight the limitations of traditional models in capturing temporal dynamics. This study is one of the first hourly population products at a large geographic extent (US), contributing to various studies that involve dynamic populations with high spatiotemporal resolution, such as air pollution exposure analysis and emergency response. |
| title | Estimating Hourly Neighborhood Population Using Mobile Phone Data in the United States |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2504.01170 |