Relaxing Dense Scatter Plots with Pixel-Based Mappings

Renata Raidou, Meister Eduard Gröller, Martin Eisemann
Relaxing Dense Scatter Plots with Pixel-Based Mappings
IEEE Transactions on Visualization and Computer Graphics, 25:1-12, March 2019.

Information

Abstract

Scatter plots are the most commonly employed technique for the visualization of bivariate data. Despite their versatility and expressiveness in showing data aspects, such as clusters, correlations, and outliers, scatter plots face a main problem. For large and dense data, the representation suffers from clutter due to overplotting. This is often partially solved with the use of density plots. Yet, data overlap may occur in certain regions of a scatter or density plot, while other regions may be partially, or even completely empty. Adequate pixel-based techniques can be employed for effectively filling the plotting space, giving an additional notion of the numerosity of data motifs or clusters. We propose the Pixel-Relaxed Scatter Plots, a new and simple variant, to improve the display of dense scatter plots, using pixel-based, space-filling mappings. Our Pixel-Relaxed Scatter Plots make better use of the plotting canvas, while avoiding data overplotting, and optimizing space coverage and insight in the presence and size of data motifs. We have employed different methods to map scatter plot points to pixels and to visually present this mapping. We demonstrate our approach on several synthetic and realistic datasets, and we discuss the suitability of our technique for different tasks. Our conducted user evaluation shows that our Pixel-Relaxed Scatter Plots can be a useful enhancement to traditional scatter plots.

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BibTeX

@article{raidou2019_prsps,
  title =      "Relaxing Dense Scatter Plots with Pixel-Based Mappings",
  author =     "Renata Raidou and Meister Eduard Gr\"{o}ller and Martin
               Eisemann",
  year =       "2019",
  abstract =   "Scatter plots are the most commonly employed technique for
               the visualization of bivariate data. Despite their
               versatility and expressiveness in showing data aspects, such
               as clusters, correlations, and outliers, scatter plots face
               a main problem. For large and dense data, the representation
               suffers from clutter due to overplotting. This is often
               partially solved with the use of density plots. Yet, data
               overlap may occur in certain regions of a scatter or density
               plot, while other regions may be partially, or even
               completely empty. Adequate pixel-based techniques can be
               employed for effectively filling the plotting space, giving
               an additional notion of the numerosity of data motifs or
               clusters. We propose the Pixel-Relaxed Scatter Plots, a new
               and simple variant, to improve the display of dense scatter
               plots, using pixel-based, space-filling mappings. Our
               Pixel-Relaxed Scatter Plots make better use of the plotting
               canvas, while avoiding data overplotting, and optimizing
               space coverage and insight in the presence and size of data
               motifs. We have employed different methods to map scatter
               plot points to pixels and to visually present this mapping.
               We demonstrate our approach on several synthetic and
               realistic datasets, and we discuss the suitability of our
               technique for different tasks. Our conducted user evaluation
               shows that our Pixel-Relaxed Scatter Plots can be a useful
               enhancement to traditional scatter plots.",
  month =      mar,
  doi =        "10.1109/TVCG.2019.2903956",
  journal =    "IEEE Transactions on Visualization and Computer Graphics",
  volume =     "25",
  pages =      "1--12",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2019/raidou2019_prsps/",
}