Contextual Anomaly Detection in Spatio-Temporal Data Using Locally Dense Regions

With the advancements in computing and location-acquisition technologies, large volumes of spatio-temporal tra-jectory data are being generated and stored. Anomaly detection in trajectory data is significant for several applications. Using a data-driven spatio-temporal context in the form of geographical sub-regions and different time-periods can enhance the relevance of detected anomalies. We propose a novel scalable contextual anomaly detection method for trajectory data using the regional density information. The effectiveness and scalability of the proposed method is shown through the empirical analysis and benchmarking with the state-of-the-art method.