Stable Visual Summaries for Trajectory Collections

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

5 Citations (Scopus)

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 languageEnglish
Title of host publicationProceedings - 2021 IEEE 14th Pacific Visualization Symposium, PacificVis 2021
PublisherIEEE Computer Society
Pages61-70
Number of pages10
ISBN (Electronic)9781665439312
DOIs
Publication statusPublished - Apr 2021
Event14th IEEE Pacific Visualization Symposium, PacificVis 2021 - Virtual, Tianjin, China
Duration: 19 Apr 202122 Apr 2021

Conference

Conference14th IEEE Pacific Visualization Symposium, PacificVis 2021
Country/TerritoryChina
CityVirtual, Tianjin
Period19/04/2122/04/21

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

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