AccuStripes: Visual exploration and comparison of univariate data distributions using color and binning

Information

• Publication Type: Journal Paper (without talk)
• Workgroup(s)/Project(s):
  • Visualization Group
• Date: April 2024
• Article Number: 103906
• DOI: 10.1016/j.cag.2024.103906
• ISSN: 1873-7684
• Journal: COMPUTERS & GRAPHICS-UK
• Volume: 119
• Publisher: PERGAMON-ELSEVIER SCIENCE LTD
• Keywords: Adaptive binning, Crowd-sourced experiment, Univariate data distributions, Visual analysis

Abstract

Understanding and analyzing univariate distributions of data in terms of their shapes as well as their specific characteristics, regarding gaps, spikes, or outliers, is crucial in many scientific disciplines. In this paper, we propose a design space composed of the visual channels position and color for representing accumulated distributions. The designs are a mixture of color-coded stripes with density lines. The width and coloring of the stripes is based on the applied binning technique. In a crowd-sourced experiment we explore a subspace, called the AccuStripes (i.e., “accumulated stripes”) design space, consisting of nine representations. These AccuStripes designs integrate three composition strategies (color only, overlay, filled curve) with three binning techniques, one uniform (UB) and two adaptive methods, namely Bayesian Blocks (BB) and Jenks’ Natural Breaks (NB). We evaluate the accuracy, efficiency, and confidence ratings of the nine AccuStripes designs for structural estimation and comparison tasks. Across all study tasks, the overlay composition was found to be most accurate and preferred by observers. Furthermore, the results demonstrate that while no binning method performed best in both identification and comparison, detection of structures using adaptive binning was the most accurate one. For validation we compared the best AccuStripes’ design, i.e., the overlay composition, to line charts. Our results show that the AccuStripes’ design outperformed the line charts in accuracy for all study tasks.

Additional Files and Images

Weblinks

• paper
• Entry in reposiTUm (TU Wien Publication Database)
• DOI: 10.1016/j.cag.2024.103906

BibTeX

@article{heim-2024-accustripes,
  title =      "AccuStripes: Visual exploration and comparison of univariate
               data distributions using color and binning",
  author =     "Anja Heim and Alexander Gall and Manuela Waldner and Eduard
               Gr\"{o}ller and Christoph Heinzl",
  year =       "2024",
  abstract =   "Understanding and analyzing univariate distributions of data
               in terms of their shapes as well as their specific
               characteristics, regarding gaps, spikes, or outliers, is
               crucial in many scientific disciplines. In this paper, we
               propose a design space composed of the visual channels
               position and color for representing accumulated
               distributions. The designs are a mixture of color-coded
               stripes with density lines. The width and coloring of the
               stripes is based on the applied binning technique. In a
               crowd-sourced experiment we explore a subspace, called the
               AccuStripes (i.e., “accumulated stripes”) design space,
               consisting of nine representations. These AccuStripes
               designs integrate three composition strategies (color only,
               overlay, filled curve) with three binning techniques, one
               uniform (UB) and two adaptive methods, namely Bayesian
               Blocks (BB) and Jenks’ Natural Breaks (NB). We evaluate
               the accuracy, efficiency, and confidence ratings of the nine
               AccuStripes designs for structural estimation and comparison
               tasks. Across all study tasks, the overlay composition was
               found to be most accurate and preferred by observers.
               Furthermore, the results demonstrate that while no binning
               method performed best in both identification and comparison,
               detection of structures using adaptive binning was the most
               accurate one. For validation we compared the best
               AccuStripes’ design, i.e., the overlay composition, to
               line charts. Our results show that the AccuStripes’ design
               outperformed the line charts in accuracy for all study
               tasks.",
  month =      apr,
  articleno =  "103906",
  doi =        "10.1016/j.cag.2024.103906",
  issn =       "1873-7684",
  journal =    "COMPUTERS & GRAPHICS-UK",
  volume =     "119",
  publisher =  "PERGAMON-ELSEVIER SCIENCE LTD",
  keywords =   "Adaptive binning, Crowd-sourced experiment, Univariate data
               distributions, Visual analysis",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2024/heim-2024-accustripes/",
}