{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T18:26:32Z","timestamp":1770834392196,"version":"3.50.1"},"reference-count":91,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,5,19]],"date-time":"2018-05-19T00:00:00Z","timestamp":1526688000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61373084"],"award-info":[{"award-number":["61373084"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Population density and distribution of services represents the growth and demographic shift of the cities. For urban planners, population density and check-in behavior in space and time are vital factors for planning and development of sustainable cities. Location-based social network (LBSN) data seems to be a complement to many traditional methods (i.e., survey, census) and is used to study check-in behavior, human mobility, activity analysis, and social issues within a city. This check-in phenomenon of sharing location, activities, and time by users has encouraged this research on gender difference and frequency of using LBSN. Therefore, in this study, we investigate the check-in behavior of Chinese microblog Sina Weibo (referred as \u201cWeibo\u201d) in 10 districts of Shanghai, China, for which we observe the gender difference and their frequency of use over a period. The mentioned districts were spatially analyzed for check-in spots by kernel density estimation (KDE) using ArcGIS. Furthermore, our results reveal that female users have a high rate of social media use, and significant difference is observed in check-in behavior during weekdays and weekends in the studied districts of Shanghai. Increase in check-ins is observed during the night as compared to the morning. From the results, it can be assumed that LBSN data can be helpful to observe gender difference.<\/jats:p>","DOI":"10.3390\/ijgi7050196","type":"journal-article","created":{"date-parts":[[2018,5,21]],"date-time":"2018-05-21T04:07:30Z","timestamp":1526875650000},"page":"196","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Using Location-Based Social Media Data to Observe Check-In Behavior and Gender Difference: Bringing Weibo Data into Play"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8903-5426","authenticated-orcid":false,"given":"Muhammad","family":"Rizwan","sequence":"first","affiliation":[{"name":"School of Communication &amp; Information Engineering, Shanghai University, Shanghai 200444, China"},{"name":"Institute of Smart City, Shanghai University, Shanghai 200444, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wanggen","family":"Wan","sequence":"additional","affiliation":[{"name":"School of Communication &amp; Information Engineering, Shanghai University, Shanghai 200444, China"},{"name":"Institute of Smart City, Shanghai University, Shanghai 200444, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ofelia","family":"Cervantes","sequence":"additional","affiliation":[{"name":"Computing, Electronics and Mechatronics Department, Universidad de las Am\u00e9ricas Puebla, Puebla 72810, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luc","family":"Gwiazdzinski","sequence":"additional","affiliation":[{"name":"Institut de G\u00e9ographie Alpine (IGA), Universit\u00e9 Grenoble Alpes, 38100 Grenoble, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"781","DOI":"10.5194\/isprsarchives-XL-1-W5-781-2015","article-title":"Intra-urban movement flow estimation using location based social networking data","volume":"40","author":"Kheiri","year":"2015","journal-title":"Int. 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