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

Rizwan Muhammad · Wanggen Wan · Ofelia Cervantes · Luc Gwiazdzinski

54 citations0/0 files storedISPRS International Journal of Geo-Information

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Human Mobility and Location-Based Analysis Geographic Information Systems Studies Urban Design and Spatial Analysis

Abstract

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.

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References

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2022Social Media Data in Urban Design and Landscape Research: A Comprehensive Literature Review 2020Analyzing Social-Geographic Human Mobility Patterns Using Large-Scale Social Media Data 2020A New Approach to Refining Land Use Types: Predicting Point-of-Interest Categories Using Weibo Check-in Data 2020Role of Big Data in the Development of Smart City by Analyzing the Density of Residents in Shanghai 2020Visualization, Spatiotemporal Patterns, and Directional Analysis of Urban Activities Using Geolocation Data Extracted from LBSN

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