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Research Article | Open Access

Temporal scatterplots

Tel-Aviv University, Tel-Aviv, Israel
Shenzhen University, Shenzhen, China
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Abstract

Visualizing high-dimensional data on a 2Dcanvas is generally challenging. It becomes significantlymore difficult when multiple time-steps are to be presented, as the visual clutter quickly increases. Moreover, the challenge to perceive the significant temporal evolution is even greater. In this paper, we present a method to plot temporal high-dimensional data in a static scatterplot; it uses the established PCA technique to project data from multiple time-steps. The key idea is to extend each individual displacement prior to applying PCA, so as to skew the projection process, and to set a projection plane that balances the directions of temporal change and spatial variance. We present numerous examples and various visual cues to highlight the data trajectories, and demonstrate the effectiveness of the method for visualizing temporal data.

Keywords

scatterplottemporal datavisual clutterprinciple component analysis (PCA)

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Computational Visual Media
Volume 6 Issue 4,
December 2020
Pages 385-400
DOI: 10.1007/s41095-020-0197-1
Cite this article:
Patashnik O, Lu M, Bermano AH, et al. Temporal scatterplots. Computational Visual Media, 2020, 6(4): 385-400. https://doi.org/10.1007/s41095-020-0197-1

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Received: 22 July 2020
Accepted: 12 September 2020
Published: 07 November 2020
© The Author(s) 2020

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