Learning Similarity Functions for Urban Events Detection by Mining Hotline Phone Records

Many cities around the world have established a platform, entitled public service hotline, to allow citizens to tell about city issues, e.g. noise nuisance, or personal encountered problems, e.g. traffic accident, by making a phone call. As a result of "crowd sensing", these records contain rich human intelligence that can help to detect urban events. In this paper, we present an event detection approach to detect urban events based on phone records. Specifically, given a set of phone records in a period of time, we first learn a similarity matrix. Each element of the matrix is estimated as the probability that the corresponding pair of records describe the same event. Then, we propose an Improved Affinity Propagation (IAP) clustering approach which takes the similarity matrix as input and generates clusters as output. Each cluster is an urban event composed of several records. Extensive experiments demonstrate the great improvement of IAP on three standard datasets for clustering and the effectiveness of our event detection approach on real data from a hotline.