Categorical Colormap Optimization with Visualization Case Studies

Hui Fang, Simon Walton, Emily Delahaye, James Harris, Dmitry Storchak, Min Chen

Research output: Contribution to journalArticle (journal)peer-review

31 Citations (Scopus)
336 Downloads (Pure)


Mapping a set of categorical values to different colors is an elementary technique in data visualization. Users of visualization software routinely rely on the default colormaps provided by a system, or colormaps suggested by software such as ColorBrewer. In practice, users often have to select a set of colors in a semantically meaningful way (e.g., based on conventions, color metaphors, and logological associations), and consequently would like to ensure their perceptual differentiation is optimized. In this paper, we present an algorithmic approach for maximizing the perceptual distances among a set of given colors. We address two technical problems in optimization, i.e., (i) the phenomena of local maxima that halt the optimization too soon, and (ii) the arbitrary reassignment of colors that leads to the loss of the original semantic association. We paid particular attention to different types of constraints that users may wish to impose during the optimization process. To demonstrate the effectiveness of this work, we tested this technique in two case studies. To reach out to a wider range of users, we also developed a web application called Colourmap Hospital.
Original languageEnglish
Article number7539556
Pages (from-to)871-880
Number of pages10
JournalIEEE Transactions on Visualization and Computer Graphics
Issue number1
Publication statusPublished - 1 Jan 2017


  • Color
  • London tube map
  • categorical colormap
  • optimization
  • seismological data visualization


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