Massive Online Geo-Social Networking Platforms and Urban Human Mobility Patterns: A Process Map for Data Collection

Abstract

Human mobility is central to our understanding of design, planning and development of civil infrastructure, particularly in urban areas where large scale mobility flow problems can critically depend on the interface between human mobility and infrastructure. Therefore, researchers have spent considerable effort to understand and predict human mobility patterns. Several recent studies have used geo-social networking platforms to examine human mobility, but the focus of these studies has been on small scale social networking media. In this study, we examined the possibility of using Twitter, a massive online social networking platform with over 400 million users, to collect human mobility data. We developed a process map to collect data from Twitter, and designed two Python modules for its implementation. A case study was conducted and its results confirmed that Twitter can provide a larger quantity of useful human mobility data. In future research, we plan to analyze the data and validate that it can accurately capture mobility patterns. This will provide insight into whether Twitter is a viable resource to study city-scale human mobility. It can also potentially deepen our understanding about the interaction between urban dwellers and civil infrastructure.