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ITEA—interactive trajectories and events analysis: exploring sequences of spatio-temporal events in movement data

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Abstract

Widespread use of GPS and similar technologies makes it possible to collect extensive amounts of trajectory data. These data sets are essential for reasonable decision making in various application domains. Additional information, such as events taking place along a trajectory, makes data analysis challenging, due to data size and complexity. We present an integrated solution for interactive visual analysis and exploration of events along trajectories data. Our approach supports analysis of event sequences at three different levels of abstraction, namely spatial, temporal, and events themselves. Customized views as well as standard views are combined to form a coordinated multiple views system. In addition to trajectories and events, we include on-the-fly derived data in the analysis. We evaluate our integrated solution using the IEEE VAST 2015 Challenge data set. A successful detection and characterization of malicious activity indicate the usefulness and efficiency of the presented approach.

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Acknowledgments

Part of this work was done in the scope of the K1 program at the VRVis Research Center.

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Authors and Affiliations

  1. VRVis Research Center, Vienna, Austria

    Lena Cibulski, Rainer Splechtna & Krešimir Matković

  2. University of Magdeburg, Magdeburg, Germany

    Lena Cibulski

  3. Virginia Tech, Blacksburg, VA, USA

    Denis Gračanin

  4. University of Buenos Aires, Buenos Aires, Argentina

    Alexandra Diehl

  5. University of Maryland, Baltimore, USA

    Mai Elshehaly

  6. University of the South, Bahía Blanca, Argentina

    Claudio Delrieux

Authors
  1. Lena Cibulski
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  2. Denis Gračanin
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  3. Alexandra Diehl
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  4. Rainer Splechtna
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  5. Mai Elshehaly
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  6. Claudio Delrieux
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  7. Krešimir Matković
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Correspondence to Lena Cibulski.

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Cibulski, L., Gračanin, D., Diehl, A. et al. ITEA—interactive trajectories and events analysis: exploring sequences of spatio-temporal events in movement data. Vis Comput 32, 847–857 (2016). https://doi.org/10.1007/s00371-016-1255-7

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  • DOI: https://doi.org/10.1007/s00371-016-1255-7

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