Multiscale dynamic human mobility flow dataset in the US during the COVID-19 epidemic
文献类型：期刊论文
| 作者 | Kang, Yuhao1; Gao, Song1; Liang, Yunlei1; Li, Mingxiao1,2,3; Rao, Jinmeng1; Kruse, Jake1 |
| 刊名 | SCIENTIFIC DATA
 |
| 出版日期 | 2020-11-12 |
| 卷号 | 7期号:1页码:13 |
| DOI | 10.1038/s41597-020-00734-5 |
| 通讯作者 | Gao, Song(song.gao@wisc.edu) |
| 英文摘要 | Understanding dynamic human mobility changes and spatial interaction patterns at different geographic scales is crucial for assessing the impacts of non-pharmaceutical interventions (such as stay-at-home orders) during the COVID-19 pandemic. In this data descriptor, we introduce a regularly-updated multiscale dynamic human mobility flow dataset across the United States, with data starting from March 1st, 2020. By analysing millions of anonymous mobile phone users' visits to various places provided by SafeGraph, the daily and weekly dynamic origin-to-destination (O-D) population flows are computed, aggregated, and inferred at three geographic scales: census tract, county, and state. There is high correlation between our mobility flow dataset and openly available data sources, which shows the reliability of the produced data. Such a high spatiotemporal resolution human mobility flow dataset at different geographic scales over time may help monitor epidemic spreading dynamics, inform public health policy, and deepen our understanding of human behaviour changes under the unprecedented public health crisis. This up-to-date O-D flow open data can support many other social sensing and transportation applications. |
| 资助项目 | National Science Foundation[BCS-2027375] ; University of Wisconsin -Madison Office of the Vice Chancellor for Research and Graduate Education ; Wisconsin Alumni Research Foundation |
| WOS研究方向 | Science & Technology - Other Topics |
| 语种 | 英语 |
| 出版者 | NATURE RESEARCH |
| WOS记录号 | WOS:000593909300003 |
| 资助机构 | National Science Foundation ; University of Wisconsin -Madison Office of the Vice Chancellor for Research and Graduate Education ; Wisconsin Alumni Research Foundation |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/156479]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Gao, Song |
| 作者单位 | 1.Univ Wisconsin, Dept Geog, GeoDS Lab, Madison, WI 53706 USA 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 3.Shenzhen Univ, Sch Architecture & Urban Planning, Shenzhen 518061, Peoples R China |
| 推荐引用方式 GB/T 7714 | Kang, Yuhao,Gao, Song,Liang, Yunlei,et al. Multiscale dynamic human mobility flow dataset in the US during the COVID-19 epidemic[J]. SCIENTIFIC DATA,2020,7(1):13. |
| APA | Kang, Yuhao,Gao, Song,Liang, Yunlei,Li, Mingxiao,Rao, Jinmeng,&Kruse, Jake.(2020).Multiscale dynamic human mobility flow dataset in the US during the COVID-19 epidemic.SCIENTIFIC DATA,7(1),13. |
| MLA | Kang, Yuhao,et al."Multiscale dynamic human mobility flow dataset in the US during the COVID-19 epidemic".SCIENTIFIC DATA 7.1(2020):13. |
入库方式： OAI收割
来源：地理科学与资源研究所
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