Urban Event Detection from Spatio-temporal IoT Sensor Data Using Graph-Based Machine Learning

The accurate detection and handling of urban events such as emergency incidents are critical to improve the safety and convenience of people's life in urban environments. Recently, it has become possible to detect and handle urban events in an effective manner by analyzing the detailed urban data collected from Internet of Things (IoT) sensors. Especially, some recent works investigated the use of spatio-temporal sensor data for urban event detection. However, we found there is one challenge of having less accuracy of detecting urban events as the granularity of processing data in the spatial dimension becomes finer. To meet the challenge, we propose a novel graph-based approach that analyzes geo-spatial characteristics of urban sensor data over time to keep the accuracy of detecting urban anomaly in finer-grained geo-spatial scales, by identifying and exploiting regions that have abnormal urban dynamics. Through experiments using real-world urban datasets, we show our approach effectively addresses the challenge and outperforms the popular machine-learning-based urban event detection methods.