Comparing mobility patterns between residents and visitors using geo-tagged social media data

Understanding the behavior of residents and visitors is vital in tourism studies, urban planning, and local economic development. However, most existing studies consider visitors as one group, while overlooking the difference in mobility patterns between subgroups of visitors and residents. In this research, we analyzed the mobility pattern of local Twitter users and visitor Twitter users, from the flow network and evenness distribution of user activities. The results show that short distance movement is the dominant type of activity not only for residents, but also for visitors. Moreover, intra-county movement accounts for the primary type of movement for all groups of Twitter users. Besides, the centrality index of Twitter users reconstructs a core–peripheral structure, and there is some relationship between the centrality index and population size. Further, the spatial distribution of evenness index at different spatial scales shows a clear “T”-shaped core–peripheral structure. However, we need to synthesize multiple open big data to improve the study and conduct the analysis in future work at finer spatial scales, such as census tracts, census blocks, or the street level.