Further Reading — Chapter 3: Color

Tier 1: Essential Reading

These sources directly underpin this chapter's core content. Read them to deepen your understanding of the science and practice of color in data visualization.

Brewer, Cynthia A. "Color Use Guidelines for Data Representation." Proceedings of the Section on Statistical Graphics, American Statistical Association (1999). The foundational paper behind ColorBrewer, establishing the empirical and perceptual basis for sequential, diverging, and qualitative palette design. Brewer's framework — matching palette type to data type — is the organizing principle of Section 3.3. If you read one paper from this list, make it this one. Available through the ASA proceedings archive.

Ware, Colin. Information Visualization: Perception for Design. 4th edition. Morgan Kaufmann, 2021. Chapters 4 and 5 cover color perception in depth, including cone physiology, opponent process theory, CIE color spaces, and the psychophysics of color discrimination. Ware grounds every recommendation in perceptual science and provides the experimental evidence behind the claims in this chapter. The Deep Dive sidebar on CIE LAB draws heavily on Ware's treatment.

Wilke, Claus O. Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures. O'Reilly Media, 2019. Chapters 4 ("Color Scales") and 19 ("Common Pitfalls of Color Use") are directly relevant. Wilke provides clear, code-adjacent explanations with rendered figure examples. Chapter 4 covers the three palette types with concrete visual examples. Chapter 19 catalogs common color mistakes — a useful complement to Section 3.6. Freely available online at clauswilke.com/dataviz.

Tier 2: Recommended Resources

These resources extend the chapter's material into specific tools, recent research, and historical context.

ColorBrewer 2.0 (colorbrewer2.org). Cynthia Brewer's indispensable web tool for selecting color palettes. Every palette is annotated for colorblind safety, print safety, and photocopy safety. The tool lets you set the number of data classes and preview palettes on a sample map. If you design charts or maps for a living, this should be bookmarked permanently.

Crameri, Fabio, Grace E. Shephard, and Philip J. Hainzl. "The Misuse of Colour in Science Communication." Nature Communications 11, article 5444 (2020). A rigorous analysis of color misuse in published scientific figures, with data showing that rainbow colormaps remain disturbingly prevalent in high-impact journals. Crameri introduces the concept of "perceptually non-uniform distortion" and quantifies how much information is lost to bad color choices. The paper also introduces the Scientific Colour Maps collection (scientificcolourmaps.org), a set of rigorously tested alternatives to rainbow.

Smith, Nathaniel J. and Stéfan van der Walt. "A Better Default Colormap for Matplotlib." SciPy 2015 talk and matplotlib Enhancement Proposal (MEP) 14. The talk and proposal that led to viridis replacing jet as matplotlib's default colormap. Smith and van der Walt explain the design criteria — perceptual uniformity, colorblind safety, monotonic luminance, aesthetic quality — and the iterative process that produced viridis, plasma, inferno, and magma. Watch the SciPy 2015 talk recording for the full context. The story is expanded in Case Study 1.

Borland, David, and Russell M. Taylor II. "Rainbow Color Map (Still) Considered Harmful." IEEE Computer Graphics and Applications 27, no. 2 (2007): 14-17. The classic polemic against the rainbow colormap, documenting its perceptual failures and the persistence of its use despite decades of evidence against it. Short, clear, and still relevant almost two decades later.

Wong, Bang. "Points of View: Color Blindness." Nature Methods 8, no. 6 (2011): 441. A concise one-page guide to designing figures for colorblind readers, published in Nature Methods' influential "Points of View" column. Provides quick rules of thumb and safe color palettes for scientific publication.

Online Tools