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Urban anomalies may result in loss of life or property if not handledproperly. Automatically alerting anomalies in their early stage or evenpredicting anomalies before happening are of great value for populations.Recently, data-driven urban anomaly analysis frameworks have been forming,which utilize urban big data and machine learning algorithms to detect andpredict urban anomalies automatically. In this survey, we make a comprehensivereview of the state-of-the-art research on urban anomaly analytics. We firstgive an overview of four main types of urban anomalies, traffic anomaly,unexpected crowds, environment anomaly, and individual anomaly. Next, wesummarize various types of urban datasets obtained from diverse devices, i.e.,trajectory, trip records, CDRs, urban sensors, event records, environment data,social media and surveillance cameras. Subsequently, a comprehensive survey ofissues on detecting and predicting techniques for urban anomalies is presented.Finally, research challenges and open problems as discussed.

Urban Anomaly Analytics: Description, Detection, and Prediction