Archive for the ‘color’ Category

Subtleties of Color (Part 6.5 of 6)

September 16th, 2013 by Robert Simmon

Drew Skau and I have received a few comments on Subtleties of Color that deserve to be included. (By the way, don’t be shy. You can leave a comment here or at

Naomi B. Robbins demonstrated that the standard land cover classification palette fails for color-deficient viewers. She also pointed out that “color-deficient” is the correct term to use, not “color-blind”. (Robbins is an expert in graphical data presentation who leads her own consulting firm, NBR, tweets from @nbrgraphs, and has blogged for Forbes.)

Comparison of land cover classification maps for normal and color-deficient viewers.
Color-deficient viewers would likely confuse red urban areas with green forests, not to mention tan scrub, in this land cover classification map of the Portland area.

Robbins is absolutely right, and I don’t think there’s an easy answer—even the Geological Survey, with its rich history of cartography, has problems displaying more than a handful of categories on a single map.

On an encouraging note, Mike Bostock sent examples (via Twitter: @mbostock) of CIE L*C*h color palettes dynamically generated by D3.js. In a web browser. (D3.js is a library that enables dynamic, interactive display of everything from time series to networks to maps. For those of us who began our careers 3 days before Netscape Navigator came out of beta, this is astonishing. Bostock created it.)

Sample D3.s color ramps.
Comparison of color ramps generated with D3.js in hue, saturation, lightness (HSL, non-perceptual); hue, chroma, lightness (HCL, perceptual); L*a*b, (Lab, perceptual); and red, green, blue (RGB, non-perceptual). D3.js allows both the selection of start and end points and interpolation in perceptual color spaces.

Here’s an example of a palette applied to a grayscale image of topography by d3.js:

Spectral palette applied to global elevation data with D3.js.
A divergent color map from D3.js (reminiscent of Color Brewer’s Spectral palette) used to display global topographic data. I do not usually recommend using a divergent palette to show sequential data, but this map cleverly uses the breakpoint (light yellow) to show median elevation. (It’s still a bit bright & saturated for my taste, though.)

I’ll post more comments if and when I get them.

Subtleties of Color
Part 1: Introduction
Part 2: The “Perfect” Palette
Part 3: Different Data, Different Colors
Part 4: Connecting Color to Meaning
Part 5: Tools & Techniques
Part 6: References and Resources for Visualization Professionals

Subtleties of Color (Part 6 of 6)

September 10th, 2013 by Robert Simmon

References and Resources for Visualization Professionals
The previous posts 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.

Data visualization bookshelf.

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’ The Interaction of Color. Itten’s text focuses on color theory (including a somewhat outdated explanation of the physiology of color perception) and the mixture of pigments.

The Interaction of Color is centered around a series of exercises performed with colored paper (replicated in an 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.

Bruce MacEvoy’s web site is primarily about painting with watercolors, and has outstanding sections on color theory and color vision.

As I’ve mentioned several times in this series, cartographers were communicating with color long before the term “data visualization” was coined.

Eduard Imhof’s Cartographic Relief Presentation explains the theory and meticulous craft applied to Swiss topographic maps (zoom in). The book covers the use of color to denote elevation and how to layer information by carefully controlling hue, saturation, and lightness.

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 (PDF) 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.

Relief Shading covers similar ground, with a straightforward section on color.

In addition to developing Color Brewer, Cynthia Brewer has written two books relevant to color and visualization. Designing Better Maps: A Guide for GIS Users is a how-to guide. Its companion volume, 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 Rainbow Color Map (Still) Considered Harmful Key quote: “The rainbow color map confuses viewers through its lack of perceptual ordering, obscures data through its uncontrolled luminance variation, and actively misleads interpretation through the introduction of non-data-dependent gradients.”

Penny Rheingans Task-based Color Scale Design (zipped PDF) Key Quote: “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? Key quote: “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 (PDF) Key quote: “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.”

Colin Ware: Color Sequences for Univariate Maps: Theory, Experiments, and Principles (PDF) Key quote: “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.

Alternate Approaches
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.


Computer Science
Bruce Linbloom provides tools to translate between color spaces—and the math behind them.

I mentioned Color Mine in my previous post, on tools. In addition to the online color-calculator, they offer libraries to convert from one color space to another.

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.

Matt Hall, of Agile Geoscience, wrote a recent trio of articles: Five Things about Colour (which includes my favorite optical illusion) and Five More Things about Colour and Colouring Maps.

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’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)

What’s Missing?
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.

Subtleties of Color
Part 1: Introduction
Part 2: The “Perfect” Palette
Part 3: Different Data, Different Colors
Part 4: Connecting Color to Meaning
Part 5: Tools & Techniques

(This series on the use of color in data visualization is being cross-posted on Thanks to Drew Skau at for the invitation.)

Subtleties of Color (Part 5 of 6)

August 28th, 2013 by Robert Simmon

Tools & Techniques: the Nuts and Bolts of Designing a Color Palette
Knowing what makes a good palette for visualization, how to find and apply good examples, or create one from scratch? In my mind the best place to start is Color Brewer. Cynthia Brewer’s tool is popular for a reason: it explains the theory behind palette design, provides excellent examples to get started with, and even displays the palettes on a sample map. The Color Brewer Palettes are also implemented in visualization applications and languages like D3, Processing, R, ArcMap, etc.

Color Brewer Screenshot
The widely-used Red-Blue divergent palette on Color Brewer.

If you don’t use software that comes with the Color Brewer palettes (Adobe Photoshop, for example), using the tables can be a bit tricky. Each color has to be specified manually, and then the individual steps need to be blended (at least if you want a smooth ramp).

In Photoshop, there’s two ways to do this: create a custom indexed color palette (good for 8-bit data, with a range of 256 values), or create a gradient map (useful for applying color to 16-bit datasets). Similar techniques should work with the GIMP and other applications.

To build an indexed palette (also called a color look-up table), start with one of the 9-color Color Brewer palettes. We need 9 colors because there are 8 divisions between each specified color, which divides evenly into the 256 available indices.

Color Brewer Red-Blue Palette

To convert the discrete colors from Color Brewer into a smooth 256-color ramp, import an 8-bit grayscale image into Photoshop. Then select Image > Mode > Indexed Color. This will bring up the Color Table window. Starting in the upper left, click and drag to select two rows, representing the first 32 colors in the palette. Then enter the Red, Green, Blue values of the first color (178, 24, 43 in our example), hit return, then enter the second color (214, 96, 77). Do this 7 more times (I know, it’s tedious) and you’ll have a full 8-bit palette. To make your life easier, Save the color table for next time (in .act format, the specification is buried on this page) before you hit OK.

You can also make a discrete palette. Just set the start and end points of the ramp to the same color.

Screen shot of Adobe Photoshop's indexed color palette.

For 16-bit data (a digital elevation model, for example), use a gradient map. Again, start by opening a grayscale file, but this time with a bit depth of 16. Then convert the grayscale image to a color one by selecting Image > Mode > RGB Color. Then add a gradient map: Layer > New Adjustment Layer > Gradient Map… Name the layer if you want, then hit OK. You’ll end up with an editable gradient map as a separate layer above your data.

A gradient map allows you to set an arbitrary number of color points to blend between, and change both the relative spacing (in increments of one percent) and relative weighting of each point. It’s a bit more flexible than an indexed color palette, but specific to Photoshop (indexed color palettes are useful in almost all visualization and graphics software). For example, to create a palette blended between 6 colors, set 6 points, each separated by a distance of 20% (see the image below). Like an indexed color palette, you can also save the gradient for future use.

Screen shot of Adobe Photoshop gradient map.

While Color Brewer is great (I use the palettes frequently) it’s not comprehensive. The available palettes are a bit limited in contrast, the selection of single- or two-color palettes is small, the lightness and saturation of the end points don’t match between palettes (making it impossible to make direct comparisons between similar datasets), and the Munsell color space doesn’t have perceptually linear saturation.

The next logical step is the NASA Ames Color Tool. (Note: When OS X 10.6 was released, the standard gamma for Macs changed from 1.8 to 2.2. Use the “Color Tool, Gamma = 2.2 (PC)” option on both Macs and PCs.) Developed to support the creation of legible aviation charts, the Ames Tool converts between the CIE L*C*h color space and RGB values. Lightness varies between 0 and 100 in increments of 10, chroma (saturation) from 0 to 156, also in increments of 10, and hue from 0 to 360 in 1˚ steps.

Screenshot of NASA Ames color tool.

I find it easiest to create palettes by typing in the hue manually, then picking a color with the appropriate lightness and chroma. Move diagonally across the array of colors, and change the hue in even increments with each step. This creates a spiral through three-dimensional color space, with hue, chroma, and lightness varying simultaneously. It’s possible to create a strictly perceptual palette by always changing saturation in the same direction (usually from dark and saturated (intense) to light and desaturated (pale)), but the available range of contrast is less than a palette that includes very dark colors. Feel free to experiment, as long as lightness varies evenly.

The Ames tool provides more flexibility than Color Brewer, but is also limited in the available range of colors. The maximum lightness with any hue is 90, the darkest 10 (inevitably with very low saturation). A suite of tools utilizing Gregor Aisch’s chroma.js Javascript library expand the number of colors, but are (in my opinion) a bit trickier to use than the Ames tool.

Screenshot of the chroma.js home page.
Chroma.js home page.

Screenshot of Tristen Brown's color picker based on chroma.js.
HCL picker, by Tristen Brown.

Screenshot of an alternative chroma.js color picker
LAB, LCH, RGB, and HSV color picker, with initial colors specified in LCH (also by Gregor Aisch). LAB allows two-color ramps, which provides additional options to create palettes appropriate for particular datasets. Gregor was kind enough to whip this up based on a few suggestion posted on Twitter. Thanks!

Another option, but sadly lacking in visual feedback, is the Online Color Converter. I haven’t used it much, but the results seem a little bit different than the equivalent conversion with chroma.js. Not too surprising, since L*C*h to RGB conversions are hardly simple.

The ColorMine color conversion interface.

The NASA Ames color tool, Lch color gradient picker, and Online Color Converter are more-or-less useful for building sequential or divergent scales. I want hue, from the médialab at Sciences Po is designed to randomly generate “maximally distinct” colors—i.e. colors for qualitative palettes. (ColorBrewer, of course, works for all three types of data.)

Screenshot of I Want Hue.
The interface for I want hue, a tool to generate qualitative color palettes derived from a perceptual color space.

The unique part about I want hue is that it can generate clusters of analogous colors—colors near each other on the color wheel. Perfect for building complex maps with large numbers of categories. It’s also useful for choosing colors for different categories of data that’s not spatial: line graphs, dot plots, or grouped bar charts, for example.

As far as I know, these tools are the state of the art. Yet each of them is limited in one way or another. Color Brewer has a fixed number of palettes. The NASA tool has a limited number of available lightness values. The chroma.js interfaces are a bit difficult to control precisely. Colormine lacks feedback. All of these tools generate a small number of colors, that then need to be blended to create smooth palettes.

What would the ideal palette building tool look like? (My ideal tool, at least.)

  • Interpolate and choose colors in a perceptual color space (i.e. CIE L*C*h). [There are a handful of color ramp generators that interpolate in L*C*h, but selection is still in RGB or HSL (Magnaview and ArcGIS, for example.)]
  • Automatically rotate through hue space, while simultaneously changing lightness and saturation. Allow clockwise and counter-clockwise rotation (red-yellow-green-blue or red-magenta-purple-blue).
  • Also allow linear interpolation through 3-dimensional color space. Changing from blue to yellow without going through green, for instance.
  • Maybe even support “weighting” of the transition: palettes that arc through color space, but aren’t a tight spiral or straight line.
  • Generate continuous as well as discrete palettes, exported as an indexed color image or color table.
  • Make the entire gamut of CIE L*C*h color space available.
  • Allow “illegal” (out of gamut) colors, but indicate when these occur.
  • Display the path of interpolation in a representation of the CIE L*C*h gamut. In 3D.
  • Support multiple display color spaces: sRGB, Adobe RGB, CMYK, etc.
  • Provide a preview image, or even allow upload of sample imagery.
  • All built with modern web technologies that provide instantaneous feedback and an elegant user interface.

Spoiler alert: I’m working on this. Drop me a line if you’re interested in collaborating.

Subtleties of Color
Part 1: Introduction
Part 2: The “Perfect” Palette
Part 3: Different Data, Different Colors
Part 4: Connecting Color to Meaning
Part 6: References & Resources for Visualization Professionals

(This series on the use of color in data visualization is being cross-posted on Thanks to Drew Skau at for the invitation.)

Subtleties of Color (Part 4 of 6)

August 19th, 2013 by Robert Simmon

Connecting Color to Meaning
Pretty much any dataset can be categorized as one of three types—sequential, divergent, and qualitative—each suited to a different color scheme. Sequential data is best displayed with a palette that varies uniformly in lightness and saturation, preferably with a simultaneous shift in hue. Divergent data is suited to bifurcated palettes with a neutral central color. Qualitative data benefits from a set of easily distinguishable colors. Palettes defined in a perceptual color space—particularly CIE L*C*h—will be more accurate than those composed using RGB or HSV color spaces, which are more suited to computers than people.

I consider those the fundamentals of color for data visualization. You’d better be certain you know what you’re doing before you violate those rules (i.e. don’t use a rainbow palette unless there’s a reason more compelling than “that’s how we’ve always done it”). What are the more subtle aspects of color for data visualization? The touches that (hopefully) put an image on the 10% side of Sturgeon’s Law (not the 90%).

Intuitive Colors
This may sound obvious, but it’s an underused principle. Whenever possible, make intuitive palettes. Some conventional color schemes, especially those used in scientific visualization, are difficult for non-experts to understand. In fact, one study found “satellite visualizations used by many scientists are not intelligible to novice users” (emphasis mine). Visualizations should be as easy as possible to interpret, so try to find a color scheme that matches the audience’s preconceptions and cultural associations.

It’s not always possible, of course (what color is electrical charge, or income?) but a fair number of datasets lend themselves to particular colors. Vegetation is green, barren ground is gray or beige. Water is blue. Clouds are white. Red, orange, and yellow are hot (or at least warm); blue is chilly.

Rainbow and naturalistic palettes applied to ocean color (roughly equivalent to ocean vegetation) data.
The unnatural colors of the rainbow palette (left) are often difficult for novice viewers to interpret. A more naturalistic palette for phytoplankton (more or less a type of ocean vegetation) trends from dark blue for barren ocean, through turquoise, green, and yellow for increasing concentrations of the tiny plants and algae.

In addition to colors affiliated with our physical environment (can you tell I primarily work on Earth science datasets?), cultural values are linked to certain colors. Check out the quilted thumbnails of Google image search results for words like “clean” (mint green) “malevolent” (ochre) and “abstract” (blue). Use these relationships to add cues into a visualization. Be aware that these are not universal, but vary by culture.

The cultural associations of color, as seen by a Google Image search.
The cultural associations of color (at least in English), derived via Google image search. Image by John Nelson, IDV solutions.

The combination of two or more datasets often tell a story better than a single dataset, and the best visualizations tell stories. The color schemes for multiple datasets displayed together need to be designed together, and complement one another.

One approach is to layer datasets together, which is pretty much impossible if you’ve already used all the colors of the rainbow to display a single dataset. (I know I harp on the rainbow palette, and I’m sure you’re tired of it, but despite the well known flaws it’s still used in a disproportionate amount of visualization.) Instead, use muted colors to limit the range of hues and contrast in one dataset, and then overlay additional data, such as the contour lines and shaded relief of a topographic map combined with land cover, roads, and buildings.

Vintage USGS topographic map of Chattanooga, Tennessee.
Muted colors, subtle shading and thin contour lines allow multiple types of data to be layered together in this 1958 topographic map of Chattanooga, Tennessee. (Did I mention cartographers have been doing this for ages, and are really good at it?)

Complementary Datasets
Other types of complementary data aren’t co-located: maps that include ocean and land, for example. In those cases careful control of lightness, saturation, and hue can enable quick differentiation as well as (mostly) accurate comparisons between datasets. Use two different hues, and vary the lightness and saturation simultaneously.

Global map of Net Primary Productivity, on land and in the oceans.
This map shows net primary productivity [a measure of the how much plants breathe (technically the amount of carbon plants take from the atmosphere and use to grow each year)] on land and in the ocean. The two datasets are qualitatively different (phytoplankton growing in the ocean, terrestrial plants on land), but quantitatively the same. The green land NPP is easily distinguishable from the blue oceans, but the relative lightness matches for a given rate of carbon uptake.

Non-diverging Breakpoints
Some sequential datasets feature one or more physically significant quantities: freezing on a map of temperature, for example. It’s not usually appropriate to use a full divergent palette, since the data are still on a continuum. To show these transitions, keep the change in lightness consistent throughout the palette, but introduce an abrupt shift in hue and/or saturation at the appropriate point. This does a good job of preserving patterns (again, one of the strengths of visualizations) while emphasizing and differentiating particular ranges of data.

Map of Sea Surface Temperature that shows areas that sustain tropical cyclones.
Hurricanes and other tropical cyclones are able to form and strengthen in waters over 82˚ Fahrenheit. This ocean temperature map uses rose and yellow to distinguish the warm waters that can sustain tropical cyclones from cool water, colored blue. (Map based on Microwave OI SST Data from Remote Sensing Systems.)

Use Color to Separate Data from Non-Data
Since color attracts the eye, lack of color can cause areas of a graphic to recede into the background. This is an extremely useful tool for creating hierarchy in a visualization. After all, you want viewers to focus on what’s important. Areas of no data are almost always less important than valid data points, but it’s still essential to include them in a visualization. Simply choosing not to color areas of no data, but assigning them a shade of gray (or even pure black and white) simultaneously de-emphasizes missing data and separates it from data. Just be careful to choose a shade of gray that’s distinct from the adjacent data.

Gray fill values separate land from ocean in this vegetation map.
Missing or invalid data should be clearly separated from valid data. Simply replacing the light beige used to represent water in this map of land vegetation (left) with gray causes the land surfaces to stand out. (Vegetation maps adapted from the NOAA Environmental Visualization Laboratory.)

Sometimes it just comes down to a judgement call. I developed a special temperature map showing the hottest land temperatures over the course of a year, so the entire map had to feel “hot” but the merely warm areas were well over 40 degrees Celsius cooler than the hottest spots on Earth. Pale yellow to deep red felt like an obvious choice. It was reasonably intuitive, and the very hottest points stood out well from the lighter areas. The brain moves the bright red areas into the foreground, and pushes the pale yellows into the background.

Map of the world's highest surface temperatures.
Warm colors, ranging from pale yellow to blood red, were most appropriate for this map of Earth’s hottest places.

When I applied the same color scale to a single day’s data, however, I was surprised to see that the coolest areas (irrigated fields) were subjectively hotter than their surroundings. The relatively small areas of pale yellow, surrounded by larger expanses of darker red, moved into the image foreground. (A combination of the compact areas of light color and very sharp boundaries, I think.) This subverted my intentions for the image. I ended up reverting to my standard blue-purple-red-yellow temperature palette, even though blue indicated temperatures of at least 30 degrees C (86 Fahrenheit)!

Two color scales applied to surface temperature maps in the Turpan desert, China.
Using a yellow-red palette, cool irrigated fields appear warmer than the nearby dunes, inverting the true relationship. A palette that runs from blue through red to yellow reads more naturally.

Aesthetics matter: attractive things work better.
Donald Norman, Emotional Design.

Most of these suggestions on the use of color are based on the principles of perception, which are derived from the neurological basis of how we see. They provide the foundation for accurate data visualization. But what separates “adequate” from “good” from “great” isn’t a matter of following rules—it’s a matter of aesthetics and judgement.

Follow good design practice as well as good visualization practice when developing imagery. In addition to color, consider the other aspects of design: typography, line, shape, alignment, etc. Be aware of the media you’re designing for. It may be trite, but a good visualization is better than the sum of its parts. Be aware of how the various elements of your design fit together. How do the colors used for the data interact with labels? With any nearby graphical elements? Are you designing for the web, television, or print? All of these considerations should inform your decisions.

Unfortunately I can’t provide any hard and fast rules to design visualizations that are aesthetically pleasing (or even beautiful). I can only encourage you to keep your eyes open. Look for good design, good art, and good visualization. Figure out why it works, and incorporate those elements into your own projects.

Subtleties of Color
Part 1: Introduction
Part 2: The “Perfect” Palette
Part 3: Different Data, Different Colors
Part 5: Tools & Techniques
Part 6: References & Resources for Visualization Professionals

(This series on the use of color in data visualization is being cross-posted on Thanks to Drew Skau at for the invitation.)

Subtleties of Color (Part 3 of 6)

August 12th, 2013 by Robert Simmon

Different Data, Different Colors
There are several types of data, each suited to different types of display. Continuously varying data, called sequential data, is the most familiar. In addition to sequential, Cynthia Brewer defines two additional types of data: divergent and qualitative. Divergent data has a “break point” in the center, often signifying a difference. For example, departure from average temperature, population change, or electric charge. Qualitative data is broken up into discrete classes or categories, as in land cover or political affiliation.

Sequential data (discussed in depth in my previous post, The “Perfect” Palette) is best represented by color palettes that vary evenly from light to dark, or dark to light, often with a simultaneous shift in hue and/or saturation.

Map of evapotranspiration with a color scheme that varies from yellow to blue.
Sequential data lies along a smooth continuum, and is suited to a palette with a linear change in lightness, augmented by simultaneous shifts in hue and saturation.

Divergent Data
Data that varies from a central value (or other breakpoint) is known as divergent or bipolar data. Examples include profits and losses in the stock market, differences from the norm (daily temperature compared to the monthly average), change over time, or magnetic polarity. In essence, there’s a qualitative change in the data (often a change in sign) as it crosses a threshold.

In divergent data, it’s usually more important to differentiate data on either side of the breakpoint—increase versus a decrease, acid versus base—than small variations in the data. Bipolar data is suited to a palette that uses two different hues that vary from a central neutral color. Essentially, two sequential palettes with equal variation in lightness and saturation are merged together. This type of palette works because it takes advantage of pre attentive processing: our visual systems can discriminate the different colors quickly and without conscious thought.

Example of two divergent palettes.
Divergent palettes, each composed of two sequential palettes merged with a neutral color. (Derived from the NASA Ames Color Tool (top) and Color Brewer.)

For the most part use white or light gray as the central shade. Although neutral, black or dark gray is typically a poor choice because the most extreme values will be light and desaturated, deemphasizing them. Central colors with a hue component, even a slight one, will tend to be associated with one end of the scale or the other.

A solar magnetogram displayed with a blue-yellow-red divergent palette derived from Color Brewer
A magnetogram is a map of magnetic fields, in this case on the surface of the Sun. A divergent palette suits this data because the north polarity (red) and south polarity (blue) are both measurements of the same quantity (magnetism), just with opposite signs. SDO HMI image adapted from the Solar Data Analysis Center.

I find it much more difficult to design divergent palettes than sequential palettes. There’s a limited number of color pairs that allow strong contrast simultaneously in both hues. If the colors converge too abruptly, high-contrast “rivers” of white may appear in the visualization when quantities are near the transition point. Even worse, about 5 percent of people (almost all of them men) are color blind, and will have a difficult time seeing the difference in certain hue pairs, particularly red-green (more rarely blue-red).

Examples of a palette that fails for colorblind viewers, and a corrected version.
Despite our best intentions, the Earth Observatory long used a vegetation anomaly palette that was completely unreadable by color blind viewers. Compare the full color palettes to what a color deficient viewer would see (derived from Adobe Photoshop’s deuteranopia simulation).

A sequential palette that varies uniformly in lightness will still be readable by someone with color deficient vision (or a black and white print), regardless of the hue. But a divergent palette with matched lightness can be difficult or impossible to parse if the viewer can’t distinguish the hues. To ensure your designs are accessible, choose from the color blind safe palettes on Color Brewer, or one of the online color blindness simulators.

Despite these difficulties, divergent palettes are worth using. In many cases, especially for trends, a difference map using a divergent palette is much more effective than an animation or even a sequence of small multiples.

Categorical Data
Qualitative data (occasionally known as categorical or thematic data) is distinct from sequential and divergent data: instead of representing proportional relationships, color is used to separate areas into distinct categories. Instead of a range of related colors, the palette should consist of colors as distinct from one another as possible. Due to the limits of perception, especially simultaneous contrast, the maximum number of categories that can be displayed is about 12 (practically speaking, probably fewer).

Examples of 12-class qualitative color schemes, derived from I want Hue and Color Brewer
These two qualitative color schemes—from I Want Hue (top) and Color Brewer (lower)—each consist of 12 distinct colors.

If you need to display double-digit categories, it’s best to group similar classes together. This is how the United States Geological Survey presents the 16 classes of the National Land Cover Database. Four urban densities are shown in shades of red, 3 forest types in shades of green, and different types of cropland in yellow and brown.

USGS map of land cover classification in and around Portland, Oregon
A grouped color scheme allows the USGS to simultaneously show 16 different land cover classes in a single map of the area surrounding Portland, Oregon.

For even larger numbers of categories, incorporate additional elements like symbols, hatching, stippling, or other patterns. Also, label each element directly. It’s impossible to distinguish dozens of colors and shapes simultaneously. Geological maps can have more than 100 categories yet remain (somewhat) readable.

Subtleties of Color
Part 1: Introduction
Part 2: The “Perfect” Palette
Part 4: Connecting Color to Meaning
Part 5: Tools & Techniques
Part 6: References & Resources for Visualization Professionals

(This series on the use of color in data visualization is being cross-posted on Thanks to Drew Skau at for the invitation.)

Qualitative vs. Sequential Color Scales

May 20th, 2011 by Robert Simmon

Sticking to the flood theme, here’s a recent map from the U.S. Army Corps of Engineers showing the predicted travel time for water in the Morganza Floodway.

Map of estimated travel times for flood water in the Morganza Floodway.

Map of estimated travel times for flood water in the Morganza Floodway. Courtesy U.S. Army Corps of Engineers.

It’s a reasonably good map, with one big flaw: the colors are more appropriate for categorical data (such as a geological map of different rock types) rather than sequential data (like elapsed time). There’s no natural progression from one color to the next, so to work out the ordering one has to rely on the position of adjacent bands of color, or look repeatedly between the map and the key. A palette that varied from light to dark, dull to saturated, or both, would be easier to read at a glance. Like this:

Example of a sequential palette.

Since I’m still not ready to do a long series of posts on colors, here’s a handout I wrote for the 2008 Access Data Workshop that covers more of the basics: Use of Color in Data Visualization. (PDF) If you’re really interested in the topic just skip what I wrote and go straight to the references:

  1. Brewer, Cynthia A. (2005) Designing Better Maps: A Guide for GIS Users. Redlands, CA: ESRI Press.
  2. MacEvoy, Bruce. (2008) Watercolors. Accessed April 28, 2008.
  3. Rogowitz, B.E.; Treinish, L.A. (1998) Data visualization: the end of the rainbow, Spectrum, IEEE , vol.35, no.12, pp.52-59.
  4. Tufte, Edward R. (1990) Envisioning Information. Cheshire, CT: Graphics Press.
  5. Ware, Colin. (2005) Information Visualization, Second Edition. San Francisco, CA: Morgan Kaufmann.


Cynthia Brewer

August 17th, 2010 by Robert Simmon

Whenever I invite someone to talk at our monthly “education and outreach” colloquia I seem to be out of town when the talk is scheduled. Sure enough, last Wednesday when Cynthia Brewer was here (at Goddard Space Flight Center, near Washington, DC) I was in Los Angeles.

Dr. Brewer is a geography professor at Penn State (as well as an author of several books on design and cartography: Designed Maps: A Sourcebook for GIS Users and Designing Better Maps: A Guide for GIS Users) specializing in research on effective map design, especially the use of color in maps. Since we make a lot of maps, ColorBrewer, her on-line tool for selecting color schemes, has been an invaluable tool. By all accounts she gave an excellent talk, I’m sorry I missed it.

ColorBrewer screenshot

A screen shot of ColorBrewer.