Discovering social relationship between city regions using human mobility

The development of a city gradually fosters different functional regions, and between these regions there exists different social information due to human activities. In this paper, a Region Activation Entropy Model (RAEM) is proposed to discover the social relations hidden between the regions. Specifically we segment a city into coherent regions according the base station (BS) position and detect the stay and passing regions in trajectories of mobile phone users. We regard one user's trajectory as a short document and take the stay regions in the trajectory as words, so that we can use Natural Language Processing (NLP) method to discover the relations between regions. Furthermore, the Region Activation Force (RAF) is defined to measure the intensity of relationship between regions. By measuring the Region Activation Entropy (RAE) based on RAF, we find an 88% potential predictability in regional mobility. The result generated by RAEM can benefit a variety of applications, including city planning, location choosing for a business and predicting the spread of human. We evaluated our method using a one-month-long record collected by mobile phone carriers. We believe our findings offer a new perspective on research of human mobility.