Research Article
Discovering Social Relationship between City Regions using Human Mobility
@INPROCEEDINGS{10.4108/eai.19-8-2015.2260775, author={Ya-Jing Xu and Chao Xue and Gong-Fu Li and An-Gen Luo and Yi-Zhe Song}, title={Discovering Social Relationship between City Regions using Human Mobility}, proceedings={11th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness}, publisher={IEEE}, proceedings_a={QSHINE}, year={2015}, month={9}, keywords={region relationship activation force entropy human mobility}, doi={10.4108/eai.19-8-2015.2260775} }- Ya-Jing Xu
Chao Xue
Gong-Fu Li
An-Gen Luo
Yi-Zhe Song
Year: 2015
Discovering Social Relationship between City Regions using Human Mobility
QSHINE
IEEE
DOI: 10.4108/eai.19-8-2015.2260775
Abstract
The development of a city gradually fosters different functional regions, and between these regions there exists different social information due to human activities, which can help us understand the human diffusion laws and predict the spread of human. In this paper, a Region Activation Entropy Model (RAEM) is proposed to evaluate and measure the social relations hidden between the regions, thereby discover human diffusion trend in hourly granularity. 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 mobile recommendation. 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.
