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Lookup NU author(s): Professor Nick HollimanORCiD, Dr Sara Fernstad, Dr Mike Simpson, Dr Kevin Wilson
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Background—It is possible to find many different visual representations of data values in visualizations, it is less common to see visual representations that include uncertainty, especially in visualizations intended for non-technical audiences. Objective—our aim is to rigorously define and evaluate the novel use of visual entropy as a measure of shape that allows us to construct an ordered scale of glyphs for use in representing both uncertainty and value in 2D and 3D environments. Method— We use sample entropy as a numerical measure of visual entropy to construct a set of glyphs using R and Blender which vary in their complexity. Results—A Bradley-Terry analysis of a pairwise comparison of the glyphs shows participants (n=19) ordered the glyphs as predicted by the visual entropy score (linear regression R2 >0.97, p<0.001). We also evaluate whether the glyphs can effectively represent uncertainty using a signal detection method, participants (n=15) were able to search for glyphs representing uncertainty with high sensitivity and low error rates. Conclusion—visual entropy is a novel cue for representing ordered data and provides a channel that allows the uncertainty of a measure to be presented alongside its mean value.
Author(s): Holliman NS, Coltekin A, Fernstad SJ, Simpson MD, Wilson KJ, Woods AJ
Publication type: Working Paper
Publication status: Published
Journal: arXiv
Type of Article: Preprint archive
Year: 2019
Publisher: arXiv
URL: https://arxiv.org/abs/1907.12879
