Stable Visual Summaries for Trajectory Collections Article Swipe
Jules Wulms
,
Juri Buchmüller
,
Wouter Meulemans
,
Kevin Verbeek
,
Bettina Speckmann
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/pacificvis52677.2021.00016
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/pacificvis52677.2021.00016
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.
Related Topics
Concepts
Computer science
Parameterized complexity
Stability (learning theory)
Trajectory
Dimensionality reduction
Principal component analysis
Reduction (mathematics)
Set (abstract data type)
Curse of dimensionality
Quality (philosophy)
Dimension (graph theory)
Data mining
Visualization
Spatial database
Algorithm
Spatial analysis
Artificial intelligence
Mathematics
Machine learning
Statistics
Programming language
Philosophy
Geometry
Epistemology
Physics
Astronomy
Pure mathematics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/pacificvis52677.2021.00016
- OA Status
- gold
- Cited By
- 8
- References
- 37
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3171804380
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3171804380Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/pacificvis52677.2021.00016Digital Object Identifier
- Title
-
Stable Visual Summaries for Trajectory CollectionsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-04-01Full publication date if available
- Authors
-
Jules Wulms, Juri Buchmüller, Wouter Meulemans, Kevin Verbeek, Bettina SpeckmannList of authors in order
- Landing page
-
https://doi.org/10.1109/pacificvis52677.2021.00016Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://research.tue.nl/en/publications/4cf9754e-bc28-4d93-9888-ecfc1e20be60Direct OA link when available
- Concepts
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Computer science, Parameterized complexity, Stability (learning theory), Trajectory, Dimensionality reduction, Principal component analysis, Reduction (mathematics), Set (abstract data type), Curse of dimensionality, Quality (philosophy), Dimension (graph theory), Data mining, Visualization, Spatial database, Algorithm, Spatial analysis, Artificial intelligence, Mathematics, Machine learning, Statistics, Programming language, Philosophy, Geometry, Epistemology, Physics, Astronomy, Pure mathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
8Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 1, 2023: 2, 2022: 2, 2021: 1Per-year citation counts (last 5 years)
- References (count)
-
37Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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