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J. Wulms, J. Buchmüller, W. Meulemans, K. Verbeek, B. Speckmann:
"Stable Visual Summaries for Trajectory Collections";
Talk: 2021 IEEE 14th Pacific Visualization Symposium (PacificVis), Tianjin, China; 2021-04-19 - 2021-04-22; in: "Proceedings of the 14th IEEE Pacific Visualization Symposium", 2021 IEEE 14th Pacific Visualization Symposium (PacificVis), (2021), ISBN: 978-1-6654-3931-2; 61 - 70.

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 stateof-
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.

http://dx.doi.org/10.1109/PacificVis52677.2021.00016

https://publik.tuwien.ac.at/files/publik_300341.pdf