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Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China

Author

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  • Rizwan Muhammad

    (School of Geography, South China Normal University, Guangzhou 510631, China)

  • Yaolong Zhao

    (School of Geography, South China Normal University, Guangzhou 510631, China)

  • Fan Liu

    (School of Geography, South China Normal University, Guangzhou 510631, China)

Abstract

In a location-based social network, users socialize with each other by sharing their current location in the form of “check-in,” which allows users to reveal the current places they visit as part of their social interaction. Understanding this human check-in phenomenon in space and time on location based social network (LBSN) datasets, which is also called “check-in behavior,” can archive the day-to-day activity patterns, usage behaviors toward social media, and presents spatiotemporal evidence of users’ daily routines. It also provides a wide range of opportunities to observe (i.e., mobility, urban activities, defining city boundary, and community problems in a city). In representing human check-in behavior, these LBSN datasets do not reflect the real-world events due to certain statistical biases (i.e., gender prejudice, a low frequency in sampling, and location type prejudice). However, LBSN data is primarily considered a supplement to traditional data sources (i.e., survey, census) and can be used to observe human check-in behavior within a city. Different interpretations are used elusively for the term “check-in behavior,” which makes it difficult to identify studies on human check-in behavior based on LBSN using the Weibo dataset. The primary objective of this research is to explore human check-in behavior by male and female users in Guangzhou, China toward using Chinese microblog Sina Weibo (referred to as “Weibo”), which is missing in the existing literature. Kernel density estimation (KDE) is utilized to explore the spatiotemporal distribution geographically and weighted regression (GWR) method was applied to observe the relationship between check-in and districts with a focus on gender during weekdays and weekend. Lastly, the standard deviational ellipse (SDE) analysis is used to systematically analyze the orientation, direction, spatiotemporal expansion trends and the differences in check-in distribution in Guangzhou, China. The results of this study show that LBSN is a reliable source of data to observe human check-in behavior in space and time within a specified geographic area. Furthermore, it shows that female users are more likely to use social media as compared to male users. The human check-in behavior patterns for social media network usage by gender seems to be slightly different during weekdays and weekend.

Suggested Citation

  • Rizwan Muhammad & Yaolong Zhao & Fan Liu, 2019. "Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China," Sustainability, MDPI, vol. 11(10), pages 1-30, May.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:10:p:2822-:d:232097
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    References listed on IDEAS

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    2. Shah Jahan Miah & Huy Quan Vu & Damminda Alahakoon, 2022. "A social media analytics perspective for human‐oriented smart city planning and management," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(1), pages 119-135, January.
    3. Chengji Han & Guogang Wang & Yongxiang Zhang & Lili Song & Lizhi Zhu, 2020. "Analysis of the temporal and spatial evolution characteristics and influencing factors of China’s herbivorous animal husbandry industry," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-14, August.
    4. Chenghao Yang & Tongtong Liu, 2022. "Social Media Data in Urban Design and Landscape Research: A Comprehensive Literature Review," Land, MDPI, vol. 11(10), pages 1-22, October.
    5. Jing Wu & Xirui Chen & Shulin Chen, 2019. "Temporal Characteristics of Waterfronts in Wuhan City and People’s Behavioral Preferences Based on Social Media Data," Sustainability, MDPI, vol. 11(22), pages 1-37, November.

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