Analyzing Human Mobility and Social Relationships from Cellular Network Data

Due to geographic and social constraints, human mobility shows a high degree of temporal and spatial regularity. More recently, emerging data tagged with geographical information can be used to study human mobility patterns. People usually spend most of their time at a few key important locations, such as home and work places. And for users with a certain social connection-co-workers or co-life, they often stay at the same locations. In this paper, firstly, we use an algorithm to identify important locations. After that, we find that the similarity between the two trajectories is closely related to their proximity in the location-based social network, where users having the same important locations are connected. In order to embody social contacts in mobility, for hourly variations in mobility similarity, we apply unsupervised clustering method to identify four categories of social ties. Finally, we further propose the unsupervised method and supervised method to predict which new links will develop in a social location-based network. We believe our finding can contribute to urban planning especially in areas of functional zone, transportation infrastructure deployment and mobile network facilities development.