Modeling User Activity Space from Location-Based Social Media: A Case Study of Weibo. Issue 1 (2nd January 2021)

• Record Type: Journal Article
• Authors: Wang, Xujiao; Yuan, Yihong
• Is Part Of: Professional geographer. Volume 73:Issue 1(2021)
• Page Start: 96
• Page End: 114
• Publication Date: 2021-01-02
• Subjects: activity space modeling -- big geodata -- data quality -- location-based social media (LBSM); 活动空间建模 -- 地理大数据 -- 数据质量 -- 基于位置的社交媒体（LBSM）; calidad de los datos -- macro-geodatos -- medios sociales basados en localización (LBSM) -- modelado del espacio de actividad; Geography -- Periodicals; 910
• DOI: 10.1080/00330124.2020.1803090
• Languages: English
• ISSNs: 0033-0124

Abstract

Abstract : Activity space studies are beneficial for discovering meaningful activity patterns and providing a deeper understanding of human behaviors. There is insufficient research, however, on how reliable location-based social media (LBSM) is as a new data source for discovering user activity spaces. To this end, this research calculates four external and three internal activity space indicators based on Weibo data from three Chinese cities. We compared the strengths and weaknesses of these indicators for approximating user activity spaces from LBSM data with a low sampling resolution. We also tested how different amounts of check-in data affect the calculation of these activity space indicators. The results provide a useful reference for future experimental design in human activity modeling based on social media data.