@inproceedings{Wulms2021Stabl-54529,
  year={2021},
  doi={10.1109/PacificVis52677.2021.00016},
  title={Stable Visual Summaries for Trajectory Collections},
  isbn={978-1-66543-931-2},
  issn={2165-8765},
  publisher={IEEE},
  address={Piscataway, NJ},
  booktitle={Proceedings : 2021 IEEE 14th Pacific Visualization Symposium : PacificVis 2021},
  pages={61--70},
  author={Wulms, Jules and Buchmüller, Juri F. and Meulemans, Wouter and Verbeek, Kevin and Speckmann, Bettina}
}

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    <dc:creator>Buchmüller, Juri F.</dc:creator>
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    <dc:contributor>Speckmann, Bettina</dc:contributor>
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    <dc:creator>Wulms, Jules</dc:creator>
    <dcterms:title>Stable Visual Summaries for Trajectory Collections</dcterms:title>
    <dcterms:abstract xml:lang="eng">The availability of devices that track moving objects has led to an explosive growth in trajectory data. When exploring the resulting large trajectory collections, visual summaries are a useful tool to identify time intervals of interest. A typical approach is to represent the spatial positions of the tracked objects at each time step via a one-dimensional ordering; visualizations of such orderings can then be placed in temporal order along a time line. There are two main criteria to assess the quality of the resulting visual summary: spatial quality - how well does the ordering capture the structure of the data at each time step, and stability - how coherent are the orderings over consecutive time steps or temporal ranges?In this paper we introduce a new Stable Principal Component (SPC) method to compute such orderings, which is explicitly parameterized for stability, allowing a trade-off between the spatial quality and stability. We conduct extensive computational experiments that quantitatively compare the orderings produced by ours and other stable dimensionality-reduction methods to various state-of-the-art approaches using a set of well-established quality metrics that capture spatial quality and stability. We conclude that stable dimensionality reduction outperforms existing methods on stability, without sacrificing spatial quality or efficiency; in particular, our new SPC method does so at a fraction of the computational costs.</dcterms:abstract>
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    <dc:creator>Meulemans, Wouter</dc:creator>
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    <dc:contributor>Verbeek, Kevin</dc:contributor>
    <dc:creator>Verbeek, Kevin</dc:creator>
    <dc:contributor>Wulms, Jules</dc:contributor>
    <dc:contributor>Buchmüller, Juri F.</dc:contributor>
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