{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T16:34:13Z","timestamp":1778690053161,"version":"3.51.4"},"reference-count":44,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,2,18]],"date-time":"2022-02-18T00:00:00Z","timestamp":1645142400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1841403"],"award-info":[{"award-number":["1841403"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Understanding the space-time dynamics of human activities is essential in studying human security issues such as climate change impacts, pandemic spreading, or urban sustainability. Geotagged social media posts provide an open and space-time continuous data source with user locations which is convenient for studying human movement. However, the reliability of Chinese geotagged social media data for representing human mobility remains unclear. This study compares human movement data derived from the posts of Sina Weibo, one of the largest social media software in China, and that of Baidu Qianxi, a high-resolution human movement dataset from \u2018Baidu Map\u2019, a popular location-based service in China with 1.3 billion users. Correlation analysis was conducted from multiple dimensions of time periods (weekly and monthly), geographic scales (cities and provinces), and flow directions (inflow and outflow), and a case study on COVID-19 transmission was further explored with such data. The result shows that Sina Weibo data can reveal similar patterns as that of Baidu Qianxi, and that the correlation is higher at the provincial level than at the city level and higher at the monthly scale than at the weekly scale. The study also revealed spatial variations in the degree of similarity between the two sources. Findings from this study reveal the values and properties and spatiotemporal heterogeneity of human mobility data extracted from Weibo tweets, providing a reference for the proper use of social media posts as the data sources for human mobility studies.<\/jats:p>","DOI":"10.3390\/ijgi11020145","type":"journal-article","created":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T08:18:05Z","timestamp":1645431485000},"page":"145","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Assessing Reliability of Chinese Geotagged Social Media Data for Spatiotemporal Representation of Human Mobility"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9876-8506","authenticated-orcid":false,"given":"Lingbo","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Urban Planning, School of Urban Design, Wuhan University, Wuhan 430072, China"},{"name":"Center for Geographic Analysis, Harvard University, Cambridge, MA 02138, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ru","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Urban Planning, School of Urban Design, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4813-7546","authenticated-orcid":false,"given":"Weihe Wendy","family":"Guan","sequence":"additional","affiliation":[{"name":"Center for Geographic Analysis, Harvard University, Cambridge, MA 02138, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuming","family":"Bao","sequence":"additional","affiliation":[{"name":"China Data Institute, Ann Arbor, MI 48108, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanchen","family":"Yu","sequence":"additional","affiliation":[{"name":"Center for Geographic Analysis, Harvard University, Cambridge, MA 02138, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaokang","family":"Fu","sequence":"additional","affiliation":[{"name":"Center for Geographic Analysis, Harvard University, Cambridge, MA 02138, USA"},{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China"},{"name":"Geocomputation Center for Social Sciences, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1446-2914","authenticated-orcid":false,"given":"Hongqiang","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.1080\/13658816.2017.1301455","article-title":"Global multi-layer network of human mobility","volume":"31","author":"Belyi","year":"2017","journal-title":"Int. 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