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Temporal scatterplots


Visualizing high-dimensional data on a 2D canvas is generally challenging. It becomes significantly more 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.


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This work was supported in part by the Israel Science Foundation (Grant No. 2366/16 and 2472/17).

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Corresponding author

Correspondence to Min Lu.

Additional information

Or Patashnik is a computer science M.Sc. student in Tel-Aviv University. She received her B.Sc. cum laude in computer science and mathematics from Tel-Aviv University in 2015.

Min Lu is an assistant professor at Shenzhen University. She received her B.Sc. degree in computer engineering from Beijing Normal University, China, in 2011, and received her Ph.D. degree in computer science from EECS, Peking University in 2017. Her major research interests include visualization methodology and visual analytics. More information can be found at

Amit H. Bermano has been a senior lecturer (assistant professor) in the School of Computer Science in Tel-Aviv University since 2018. Previously, he was a postdoctoral researcher in the Princeton Graphics Group (2016-2018), and a postdoctoral researcher at Disney Research Zurich (2015). He conducted his Ph.D. studies at ETH Zurich in collaboration with Disney Research Zurich (2011-2015). His master and bachelor degrees were obtained at the Technion—Israel Institute of Technology.

Daniel Cohen-Or is a professor in the School of Computer Science. He received his B.Sc. cum laude in mathematics and computer science (1985), and his M.Sc. cum laude in computer science (1986) from Ben-Gurion University, and his Ph.D. degree from the Department of Computer Science (1991) of the State University of New York at Stony Brook. He is on the editorial boards of a number of international journals, and a member of many program committees of international conferences. He was the recipient of a Eurographics Outstanding Technical Contributions Award in 2005, and an ACM SIGGRAPH Computer Graphics Achievement Award in 2018. In 2013 he received the People’s Republic of China Friendship Award. In 2015 he was named a Thomson Reuters Highly Cited Researcher. In 2019 he won the Kadar Family Award for Outstanding Research. In 2020 he received the Eurographics Distinguished Career Award. His research interests are in computer graphics, in particular, synthesis, processing, and modeling techniques. His current main interests are in image synthesis, motion and transformations, shapes, surfaces, analysis and reconstruction, and information visualization.

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Patashnik, O., Lu, M., Bermano, A.H. et al. Temporal scatterplots. Comp. Visual Media 6, 385–400 (2020).

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  • scatterplot
  • temporal data
  • visual clutter
  • principle component analysis (PCA)