About a year ago, we published a blog post framed as a letter to NASA, asking them to stop using rainbow color scales. The post was written out of a general frustration with rainbow color scales, but especially out of seeing field experts and leaders, like NASA, using a perceptually incorrect color scale. We weren’t alone. Robert Simmon from NASA’s Earth Observatory has been crusading for the same changes. He’s made great progress, and as a continuation of that, he’s responding to our “letter” with a brilliant series of blog posts on proper use of colors and color scales.
The previous posts in the Subtleties of Color series are my personal take on using color in visualization. I hope my perspective is useful, but it’s primarily a synthesis of the work of others. Here’s a list of the sources that inspired and informed this series. If you don’t have the time or the inclination to sort through the whole set, I think these three resources are essential:
Colin Ware’s Information Visualization: Perception for Design, which has several sections on vision, light, and color; the “Color and Information” chapter in Edward Tufte’s Envisioning Information, and the supplementary information in Cynthia Brewer’s ColorBrewer tool.
Artists (particularly painters) have long been concerned, possibly better described as obsessed, with color. Two Twentieth-Century classics stand out: Johannes Itten’s The Elements of Color and Josef Albers’ iPad app developed by Yale University) a concrete demonstration of simultaneous contrast, simulated transparency, and perceptual versus mathematical use of color. It’s the definitive guide to the relativity of color.
As I’ve mentioned several times in this series, cartographers were communicating with color long before the term “data visualization” was coined.
Tom Patterson, a cartographer with the National Park Service, updates Imhof’s ideas on Shaded Relief: Ideas and Techniques about Relief Presentation on Maps. This web site shows how to use modern techniques and data to replicate classic map designs. I find The Development and Rationale of Cross-blended Hypsometric Tints particularly fascinating. It describes Patterson’s technique of using two color palettes to denote elevation on a single map: one optimized for arid areas, the other for humid ones. (pull from Natural Earth)
In addition to developing Color Brewer, Cynthia Brewer has written two books relevant to color and visualization. Designed Maps: A Sourcebook for GIS Users teaches by example, showcasing well-designed maps, and describing why they are effective.
Written in 1967, Jacques Bertin’s Semiology of Graphics laid out what may have been the first comprehensive, perception-based theory of visualization. The section on color is short, but essential.
Edward Tufte may be dogmatic, perhaps even a bit curmudgeonly, but he makes an elegant case for his point of view. The chapter on color in Envisioning Information is dense and convincing, packing an entire textbook’s worth of information into 16 pages.
Maureen Stone’s A Field Guide to Digital Color could probably fit in any one of these categories. It covers everything from color theory, the physiology of vision, color management, and data visualization.
Scientists Studying Vision, Perception, and Visualization
As I’ve hopefully made clear, a perceptual approach to color in data visualization isn’t a matter of aesthetics or personal taste: it’s based on research into human vision and understanding. Here are some of the papers and books I’ve found most helpful.
Borland and Taylor Task-based Color Scale Design
- “There are no hard and fast rules in the design of color scales. … The actual answers must come from the visualization designer after consideration of relevant factors, and perhaps with a bit of divine inspiration. In the end, the true test of the value of a color scale is simply ‘Does it work?’”
Bernice Rogowitz and Lloyd Treinish authored two key papers in the 1990s: How NOT to Lie with Visualization and Why Should Engineers and Scientists Be Worried About Color?
- “At the core of good science and engineering is the careful and respectful treatment of data.”
Spence, et al. Using Color to Code Quantity in Spatial Displays
- “Linear variation in brightness and saturation facilitates simple tasks such as magnitude estimation or paired comparisons, and the addition of hue enhances performance with more complex cognitive tasks.”
- “In general, the form information displayed in a univariate map is far more important than the metric information. Absolute quantities are well represented in a table, whereas maps gain their utility from their ability to display the ridges and valleys, cusps, and other features.”
Ware’s two books, Information Visualization: Perception for Design and Visual Thinking for Design are also excellent resources. Information Visualization is more thorough, Visual Thinking for Design is more concise.
Dave Green, a Senior Lecturer with the Department of Physics of the University of Cambridge came up with the cubehelix palette. It varies perceptually in lightness and rotates around the hue circle one and a half times, contributing additional contrast. There’s also a tool to generate variations on this palette.
Another take is Matteo Niccoli’s perceptual rainbow. Niccoli provides a brilliant deconstruction of the weaknesses of the traditional rainbow palette, and he developed an alternative with linear change in lightness, but retains many of the saturated colors that appeal to those accustomed to the rainbow palette.
Instead of interpolating between colors in lightness, chroma, and hue space; Gregor Aisch’s brand-new additions to chroma.js use bezier curves and lightness adjustments to smooth and linearize palettes. He also included a way to pick intermediate hues, which adds some welcome flexibility to palette-building.
And Kenneth Moreland of Sandia National Laboratories developed algorithms to generate perceptual divergent palettes.
Bruce Linbloom provides tools to translate between color spaces—and the math behind them.
And here’s an approach to scientifically determining the semantic associations of color, by Sharon Lin et al.:
Selecting Semantically-Resonant Colors for Data Visualization.
There’s a large (and growing) community of data visualizers on the web, all of them eager to share ideas. I’m indebted to them.
Robert Kosara summarizes many of these issues on his blog Eager Eyes, with How the Rainbow Color Map Misleads.
I’ve already pointed to Gregor Aisch’s chroma.js, but he’s also got a concise critique of HSV-derived palettes.
Theresa-Marie Rhyne covers additional color spaces (Red, Yellow, Blue; Cyan, Magenta, Yellow, Black) and types of color schemes (monochromatic, complementary, analogous) in her post Applying Artistic Color Theories to Visualization.
Andy Kirk, Visualizing Data: Tools and Resources for Working with Colour.
Dundas Data Visualization provides one of the best explanations of the challenges of designing for the color blind I’ve seen with Visualizing for the Color Blind.
Three posts by Visually’s own Drew Skau explain why NASA should stop relying on the spectrum to display data Dear NASA: No More Rainbow Color Scales, Please provide tips for Building Effective Color Scales, and explore the psychology of color Seeing Color Through Infographics and Data Visualizations.
Many of these bloggers are active on Twitter:
Matteo Niccoli @My_Carta
Robert Kosara @eagereyes
Matt Hall @kwinkunks
Andy Kirk @visualisingdata
Gregor Aisch @driven_by_data
Drew Skau @seeingstructure
Naomi Robbins @nbrgraphs
Mike Bostock @mbostock
even Edward Tufte @EdwardTufte
(and me @rsimmon)
I’m sure I’ve missed some important resources for learning and using color. For example, I’ve misplaced the first book I read on color vision, and I can’t recall the title. There are many other resources available, please point them out in the comments.
Robert Simmon is a data visualizer and designer for NASA’s Earth Observatory. With 19 years of experience at NASA, he is an expert at creating clear and compelling imagery from satellite data. Robert focuses on producing visualizations that are elegant and easily understandable, while accurately presenting the underlying data. His imagery appears regularly in newspapers, web sites, and advertisements, and was featured on the login screen of the first Apple iPhone.