MoveSense: spatio-temporal Clustering Technique for Discovering Residence Change in Mobile Phone Data

The ability to detect when a person change their place of residence in a city or country is vitally important not just for urban planning but also for business intelligence. Although there are traditional approaches such as population census to collect this type of data, they have serious drawbacks. Thanks to the ubiquity of mobile phones, researchers have demonstrated that data generated from cellular network such as Call Detailed Records(CDR) can provide similar information at a relatively lower cost and higher temporal resolution. In this paper, we investigate two research questions: first, whether we can reliably discover a person's residence change from unlabeled CDR data. Second, if we can develop an algorithm that can autamatically carry out this task. To this end, we first formulate the residence change discovery problem by learning from population census approach and then propose a sequential spatio-temporal clustering technique-MoveSense to solve this problem. We use a large scale CDR dataset with over 3.5 billion call records and 16 million unique users to conduct experiments to validate our technique. We find that across the three categories of test datasets, the technique performed well with average detection rate of 71 percent, 68 percent and 72 percent.