UW Interactive Data Lab
Error rate by span and reference for assorted colormaps. Points indicate bootstrapped means, along with 50% (thick) and 95% (thin) CIs. Each sub-plot includes the mean value for each span level (dotted grey line). Viridis exhibits consistently low error across the board. The accuracy of blues matches that of viridis at larger spans, but drops notably for the smallest span. The blueorange diverging scheme exhibits errors when comparison is made across the central blue-orange hue boundary.
An essential goal of quantitative color encoding is the accurate mapping of perceptual dimensions of color to the logical structure of data. Prior research identifies weaknesses of "rainbow" colormaps and advocates for ramping in luminance, while recent work contributes multi-hue colormaps generated using perceptually-uniform color models. We contribute a comparative analysis of different colormap types, with a focus on comparing single- and multi-hue schemes. We present a suite of experiments in which subjects perform relative distance judgments among color triplets drawn systematically from each of four single-hue and five multi-hue colormaps. We characterize speed and accuracy across each colormap, and identify conditions that degrade performance. We also find that a combination of perceptual color space and color naming measures more accurately predict user performance than either alone, though the overall accuracy is poor. Based on these results, we distill recommendations on how to design more effective color encodings for scalar data.