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Open AccessArticle

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

1
School of Communication & Information Engineering, Shanghai University, Shanghai 200444, China
2
Institute of Smart City, Shanghai University, Shanghai 200444, China
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Computing, Electronics and Mechatronics Department, Universidad de las Américas Puebla, Puebla 72810, Mexico
4
Institut de Géographie Alpine (IGA), Université Grenoble Alpes, 38100 Grenoble, France
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(5), 196; https://doi.org/10.3390/ijgi7050196
Received: 24 March 2018 / Revised: 11 May 2018 / Accepted: 16 May 2018 / Published: 19 May 2018
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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MDPI and ACS Style

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. https://doi.org/10.3390/ijgi7050196

AMA Style

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 International Journal of Geo-Information. 2018; 7(5):196. https://doi.org/10.3390/ijgi7050196

Chicago/Turabian Style

Rizwan, Muhammad; Wan, Wanggen; Cervantes, Ofelia; Gwiazdzinski, Luc. 2018. "Using Location-Based Social Media Data to Observe Check-In Behavior and Gender Difference: Bringing Weibo Data into Play" ISPRS Int. J. Geo-Inf. 7, no. 5: 196. https://doi.org/10.3390/ijgi7050196

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