Resonance - An Intelligence Analysis Framework for Social Connection Inference via Mining Co-Occurrence Patterns Over Multiplex Trajectories

With the rapid development of the Internet of Things(IoT), in the last decades, law enforcement agencies have deployed extensive sensor networks for public safety purposes. Diverse kinds of trajectories from the sensor networks provide an unprecedented opportunity for intelligence analysis. The geographic co-movement pattern has rarely been used by the police force to infer social connections, although it has been prevalent in other fields. The previous studies have mainly focused on a singular form of trajectories with exact co-locations, and the spread of the co-locations is over-looked. In this paper, we propose a novel framework for detecting co-occurrence patterns over multiplex trajectories. Firstly, We constructed the foundation for the discovery of co-occurrence events, namely space-time resonance honeycomb. It consists of multiple polygonal zones over sensor networks. Secondly, we transform all trajectories into a series of space-time prisms, and co-location activities are recorded using a sliding window approach. Thirdly, we propose a novel feature: Geo-Spread, which captures the extent of the co-location spread. In the end, we combine multiple features and employ Random Forest to predict social connections. We conduct extensive experiments on both the public dataset and the real-world surveillance dataset. Experiment results on all datasets prove the effectiveness of the proposed framework by outperforming the state-of-the-art methods.