Abstract
Visualizing frequently occurring patterns and potentially unusual behaviors in trajectory can provide valuable insights into activities behind the data. In this paper, we introduce TrajViz, a motif (frequently repeated subsequences) based visualization software that detects patterns and anomalies by inducing “grammars” from discretized spatial trajectories. We consider patterns as a set of sub-trajectories with unknown lengths that are spatially similar to each other. We demonstrate that TrajViz has the capacity to help users visualize anomalies and patterns effectively.
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CITATION STYLE
Gao, Y., Li, Q., Li, X., Lin, J., & Rangwala, H. (2017). TrajViz: A Tool for Visualizing Patterns and Anomalies in Trajectory. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10536 LNAI, pp. 428–431). Springer Verlag. https://doi.org/10.1007/978-3-319-71273-4_45
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