Exploring the effectiveness of location‐based social media in modeling user activity space: A case study of Weibo. Issue 4 (28th August 2018)

• Record Type: Journal Article
• Authors: Yuan, Yihong; Wang, Xujiao
• Is Part Of: Transactions in GIS. Volume 22:Issue 4(2018)
• Page Start: 930
• Page End: 957
• Publication Date: 2018-08-28
• Subjects: Geographic information systems -- Periodicals; 910.285
• DOI: 10.1111/tgis.12450
• Languages: English
• ISSNs: 1361-1682

Abstract

Abstract: Location‐based social media (LBSM) has been widely utilized to supplement traditional survey methods in modeling human activity patterns. However, there has not been sufficient study to assess the reliability of these data in deriving human movement. This research aims to evaluate how data collection duration and sample sizes affect the reliability of LBSM data in activity modeling based on two indicators: radius of gyration (ROG) and entropy. We use a linear regression model with logarithmic transformation to approximate how the magnitude of each indicator changes with different data collection durations—from 1 to 12 months. The results indicate that both ROG and entropy increase when the amount of data increases. However, the rate of increase slows down and approaches zero eventually. We also approximated the limit values and verified that with 12‐month data, we are at approximately >95% magnitude of the limit values for both indicators in all three cities. The clustering analysis also demonstrated that there are outlier users who exhibit distinct patterns. This case study focuses on three Chinese cities (Beijing, Shanghai, and Guangzhou) and provides a useful reference to explore the balance point between data effectiveness and an appropriate sample size from LBSM data.