{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T14:18:45Z","timestamp":1784297925001,"version":"3.55.0"},"reference-count":68,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T00:00:00Z","timestamp":1673481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFE01277002"],"award-info":[{"award-number":["2019YFE01277002"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["41671412"],"award-info":[{"award-number":["41671412"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2019YFE01277002"],"award-info":[{"award-number":["2019YFE01277002"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41671412"],"award-info":[{"award-number":["41671412"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Population distribution data with high spatiotemporal resolution are of significant value and fundamental to many application areas, such as public health, urban planning, environmental change, and disaster management. However, such data are still not widely available due to the limited knowledge of complex human activity patterns. The emergence of location-based service big data provides additional opportunities to solve this problem. In this study, we integrated ambient population data, nighttime light data, and building volume data; innovatively proposed a spatial downscaling framework for Baidu heat map data during work time and sleep time; and mapped the population distribution with high spatiotemporal resolution (i.e., hourly, 100 m) in Beijing. Finally, we validated the generated population distribution maps with high spatiotemporal resolution using the highest-quality validation data (i.e., mobile signaling data). The relevant results indicate that our proposed spatial downscaling framework for both work time and sleep time has high accuracy, that the distribution of the population in Beijing on a regular weekday shows \u201ccentripetal centralization at daytime, centrifugal dispersion at night\u201d spatiotemporal variation characteristics, that the interaction between the purpose of residents\u2019 activities and the spatial functional differences leads to the spatiotemporal evolution of the population distribution, and that China\u2019s \u201csurgical control and dynamic zero COVID-19\u201d epidemic policy was strongly implemented. In addition, our proposed spatial downscaling framework can be transferred to other regions, which is of value for governmental emergency measures and for studies about human risks to environmental issues.<\/jats:p>","DOI":"10.3390\/rs15020458","type":"journal-article","created":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T05:45:33Z","timestamp":1673502333000},"page":"458","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["Mapping Population Distribution with High Spatiotemporal Resolution in Beijing Using Baidu Heat Map Data"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3510-2002","authenticated-orcid":false,"given":"Wenxuan","family":"Bao","sequence":"first","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Key Laboratory of Environmental Remote Sensing and Digital City, Beijing Normal University, Beijing 100875, China"},{"name":"Key Laboratory of Environmental Change and Natural Disaster, MOE, Beijing Normal University, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adu","family":"Gong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Key Laboratory of Environmental Remote Sensing and Digital City, Beijing Normal University, Beijing 100875, China"},{"name":"Key Laboratory of Environmental Change and Natural Disaster, MOE, Beijing Normal University, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2033-5011","authenticated-orcid":false,"given":"Tong","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiran","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Statistics, Beijing Normal University, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boyi","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Key Laboratory of Environmental Remote Sensing and Digital City, Beijing Normal University, Beijing 100875, China"},{"name":"Key Laboratory of Environmental Change and Natural Disaster, MOE, Beijing Normal University, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0074-9583","authenticated-orcid":false,"given":"Shuaiqiang","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China"},{"name":"Beijing Key Laboratory of Environmental Remote Sensing and Digital City, Beijing Normal University, Beijing 100875, China"},{"name":"Key Laboratory of Environmental Change and Natural Disaster, MOE, Beijing Normal University, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1080\/15481603.2021.1935128","article-title":"Mapping hourly population dynamics using remotely sensed and geospatial data: A case study in Beijing, China","volume":"58","author":"Zhao","year":"2021","journal-title":"Gisci. 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