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Tuesday, June 28 • 5:57pm - 6:15pm
Colour schemes in data visualisation: Bias and Precision

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The technique of mapping continuous values to a sequence of colours, is often used to visualise quantitative data. The ability of different colour schemes to facilitate data interpretation has not been thoroughly tested. Using a survey framework built with Shiny and loggr, we compared six commonly used colour schemes in two experiments: a measure of perceptually linearity and a map reading task for: (1) bias and precision in data interpretation, (2) response time and (3) colour preferences. The single-hue schemes were unbiased — perceived values did not consistently deviate from the true value, but very imprecise — large data variance between the perceived values. Schemes with hue transitions improved precision, however they were highly biased when not close to perceptually linearity (especially for the multi-hue ‘rainbow’ schemes). Response time was shorter for the single-hue schemes and longer for more complex colour schemes. There was no aesthetic preference for any of the colourful schemes. These results show that in choosing a colour scheme to communicate quantitative information, there are two potential pitfalls: bias and precision. Every use of colour to represent data should be aware of the bias--precision trade-off and select the scheme that balances these two potential communication errors.

avatar for Jacqueline Meulman

Jacqueline Meulman

Visiting Professor, Stanford University

avatar for William K. Cornwell

William K. Cornwell

UNSW, Australia
Into Ecology, Evolutionary Biology, and data visualization

Tuesday June 28, 2016 5:57pm - 6:15pm
McCaw Hall 326 Galvez Street Stanford, CA 94305-6105

Attendees (137)