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ISPRS Int. J. Geo-Inf. 2018, 7(5), 196;

Using Location-Based Social Media Data to Observe Check-In Behavior and Gender Difference: Bringing Weibo Data into Play

School of Communication & Information Engineering, Shanghai University, Shanghai 200444, China
Institute of Smart City, Shanghai University, Shanghai 200444, China
Computing, Electronics and Mechatronics Department, Universidad de las Américas Puebla, Puebla 72810, Mexico
Institut de Géographie Alpine (IGA), Université Grenoble Alpes, 38100 Grenoble, France
Author to whom correspondence should be addressed.
Received: 24 March 2018 / Revised: 11 May 2018 / Accepted: 16 May 2018 / Published: 19 May 2018
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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 “Weibo”) 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. View Full-Text
Keywords: big data; social network; lbsn; check-in; gender difference big data; social network; lbsn; check-in; gender difference

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Rizwan, M.; Wan, W.; Cervantes, O.; Gwiazdzinski, L. Using Location-Based Social Media Data to Observe Check-In Behavior and Gender Difference: Bringing Weibo Data into Play. ISPRS Int. J. Geo-Inf. 2018, 7, 196.

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