The increasing availability of spatiotemporal data continuously collected from various sources provides new opportunities for a timely understanding of the data in their spatial and temporal context. Finding abnormal patterns in such data poses significant challenges. Given that there is often no clear boundary between normal and abnormal patterns, existing solutions are limited in their capacity of identifying anomalies in large, dynamic and heterogeneous data, interpreting anomalies in their multifaceted, spatiotemporal context, and allowing users to provide feedback in the analysis loop. In this work, we introduce a unified visual interactive system and framework, Voila, for interactively detecting anomalies in spatiotemporal data collected from a streaming data source. The system is designed to meet two requirements in real-world applications, i.e., online monitoring and interactivity. We propose a novel tensor-based anomaly analysis algorithm with visualization and interaction design that dynamically produces contextualized, interpretable data summaries and allows for interactively ranking anomalous patterns based on user input. Using the "smart city" as an example scenario, we demonstrate the effectiveness of the proposed framework through quantitative evaluation and qualitative case studies.
Anomaly detection in multidimensional data is a challenging task. Detecting anomalous mobility patterns in a city needs to take spatial, temporal, and traffic information into consideration. Although existing techniques are able to extract spatiotemporal features for anomaly analysis, few systematic analysis about how different factors contribute to or affect the anomalous patterns has been proposed. In this paper, we propose a novel technique to localize spatiotemporal anomalous events based on tensor decomposition. The proposed method employs a spatial-feature-temporal tensor model and analyzes latent mobility patterns through unsupervised learning. We first train the model based on historical data and then use the model to capture the anomalies, i.e., the mobility patterns that are significantly different from the normal patterns. The proposed technique is evaluated based on the yellow-cab dataset collected from New York City. The results show several interesting latent mobility patterns and traffic anomalies that can be deemed as anomalous events in the city, suggesting the effectiveness of the proposed anomaly detection method.
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