Effects of data distribution and granularity on color semantics for colormap data visualizations

Clementine Zimnicki, Chin Tseng, Danielle Albers Szafir, Karen Schloss

Room: 104

2023-10-24T22:09:00ZGMT-0600Change your timezone on the schedule page
Exemplar figure, described by caption below
An image of 80 square colormap visualizations in 20 columns and 4 rows. They are grouped by the 10 color scales used to generate them; from left to right, ColorBrewer Red and Blue, Gray, Hot, Magma+, Mako+, Viridis, Plasma, Autumn, and Winter. Maps are also grouped by granularity (maps appear either coarse or smooth), background (maps are presented on a black or white background), and shift condition (the colors in the maps are either shifted to create large dark regions, or colors are uniformly distributed throughout the maps).
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Visual reasoning, information visualization, colormap data visualizations, color cognition


To create effective data visualizations, it helps to represent data using visual features in intuitive ways. When visualization designs match observer expectations, visualizations are easier to interpret. Prior work suggests that several factors influence such expectations. For example, the dark-is-more bias leads observers to infer that darker colors map to larger quantities, and the opaque-is-more bias leads them to infer that regions appearing more opaque (given the background color) map to larger quantities. Previous work suggested that the background color only plays a role if visualizations appear to vary in opacity. The present study challenges this claim. We hypothesized that the background color would modulate inferred mappings for colormaps that should not appear to vary in opacity (by previous measures) if the visualization appeared to have a “hole” that revealed the background behind the map (hole hypothesis). We found that spatial aspects of the map contributed to inferred mappings, though the effects were inconsistent with the hole hypothesis. Our work raises new questions about how spatial distributions of data influence color semantics in colormap data visualizations.