Abstract
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.
Original language | English |
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Title of host publication | Proceedings - 2021 IEEE 14th Pacific Visualization Symposium, PacificVis 2021 |
Publisher | IEEE Computer Society |
Pages | 61-70 |
Number of pages | 10 |
ISBN (Electronic) | 9781665439312 |
DOIs | |
Publication status | Published - Apr 2021 |
Event | 14th IEEE Pacific Visualization Symposium, PacificVis 2021 - Virtual, Tianjin, China Duration: 19 Apr 2021 → 22 Apr 2021 |
Conference
Conference | 14th IEEE Pacific Visualization Symposium, PacificVis 2021 |
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Country/Territory | China |
City | Virtual, Tianjin |
Period | 19/04/21 → 22/04/21 |
Bibliographical note
Funding Information:W. Meulemans and J. Wulms were partially supported by the Netherlands eScience Center (NLeSC); grant no. 027.015.G02. J. Wulms was partially supported by the Austrian Science Fund (FWF), grant P 31119. B. Speckmann and K. Verbeek were partially supported by the Dutch Research Council (NWO); project no. 639.023.208 and no. 639.021.541, respectively.
Publisher Copyright:
© 2021 IEEE.
Funding
W. Meulemans and J. Wulms were partially supported by the Netherlands eScience Center (NLeSC); grant no. 027.015.G02. J. Wulms was partially supported by the Austrian Science Fund (FWF), grant P 31119. B. Speckmann and K. Verbeek were partially supported by the Dutch Research Council (NWO); project no. 639.023.208 and no. 639.021.541, respectively.
Keywords
- Dimensionality reduction
- Human-centered computing
- Mathematics of computing
- Probability and statistics
- Statistical paradigms
- Visualization
- Visualization techniques