Guidelines
Suggest edit

Vary the lightness of categorical hues so categories still separate in grayscale

For categorical color palettes that must remain distinguishable in grayscale, use lightness differences across hues to improve accessibility and mitigate category collisions for readers with color-vision deficiencies.

  • purpose:refine
  • basis:heuristic
  • data:categorical
  • quality:accessibility
  • lever:encoding
  • channel:color-lightness:use
  • needs:color-vision-deficiency
  • access:contrast:use

advice

Separate categorical hues by lightness

Give categorical hues different lightness levels instead of relying on hue alone. For example, make one category noticeably lighter and another darker so the palette still works when viewed in greyscale.

reason

Why lightness variation helps

Lightness adds a second cue beyond hue. Categories stay separable when readers cannot rely on hue differences alone.

Mechanism: When categorical colors differ in both hue and lightness, readers can distinguish categories in grayscale and in color-deficient viewing conditions.

Evidence: The article says to give hues different lightnesses so they work in greyscale, look better, and become easier to distinguish, especially for colorblind readers (Muth, 2021).

context

Use when category colors must remain distinct beyond hue

  • User Goal: Keep categories distinguishable under more than one viewing condition.
  • Task: Tell categorical groups apart even when hue is weakened or unavailable.
  • Data: The color scale is categorical rather than quantitative.
  • Chart Setting: A hue palette is already being used to encode categories.
  • Audience: Readers with color-vision deficiencies or readers seeing the chart in greyscale.
  • Success Criterion: Each category remains visually distinct without relying on hue alone.

exceptions

Do not use this as a palette rule for quantitative gradients

Break it when: The color scale is a quantitative gradient rather than a set of categorical hues. Why: This rule is about separating categorical colors, not defining ordered value gradients.

costs

Tradeoffs of adding lightness variation

Sacrifice: Palette choices become more constrained because hue alone is not enough. Risk: Colors with similar lightness collapse into the same gray tone. Mitigation: Inspect the palette in greyscale and widen the lightness gaps where categories merge.

mistakes

Common palette mistake

Mistake: Choose category colors that differ mostly by hue while staying at nearly the same lightness. Why it fails: The categories become harder to distinguish, especially for colorblind readers.

check

Check the palette without hue

Failure Sign: Two or more category colors turn into nearly the same shade of gray. Quick Check: View the palette in greyscale and see whether every category still separates. Stronger Test: Compare categories by lightness alone; if two categories depend only on hue difference, revise the palette.

fix

Fix the palette

  • Increase the lightness differences between category hues.
  • Replace hues that only differ by hue with lighter or darker alternatives.
  • Recheck the palette in greyscale until each category still separates.

References

Muth, L. C. (2021). Which color scale to use when visualizing data. https://www.datawrapper.de/blog/which-color-scale-to-use-in-data-vis