Exploring human mobility patterns using geo-tagged social media data at the group level. Issue 2 (4th May 2019) - Yang, Chao; Xiao, Meng; Ding, Xuan; Tian, Wenwen; Zhai, Yong; Chen, Jie; Liu, Lei; Ye, Xinyue
Abstract: Exploring human mobility using social media data is an active research area related to many disciplines such as geography, urban planning and public health. We attempt to understand human mobility at the community level by analysing community mobility patterns across a significant time-span based on geo-tagged social media data. We take a typical college community, The Chinese University of Geosciences Wuhan (CUG Wuhan) community, as our research object. We identify more than 3757 CUG members with 81, 059 geo-tagged Weibo messages in the period 2014–2015. We measure CUG group mobility patterns by analysing the spatio-temporal distribution of these messages and the activity patterns. We find that: (i) the students' 'active area' in relation to their distance from CUG obeys a power-law distribution; (ii) heavy 'check-in' Weibo users do not, in fact, tend to be more active (in the physical world) than light 'check-in' Weibo users; (iii) mobility differences reflect gender differences.