Guidelines
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Avoid dark yellowish-green colors when optimizing categorical palette preference

For aesthetic evaluation of categorical palettes, avoid dark yellowish-green hues on categorical color encodings to maximize aesthetics and mitigate disliked color-region choices for viewers judging palette appeal.

  • purpose:refine
  • basis:empirical
  • data:categorical
  • quality:aesthetics
  • lever:encoding
  • polish:palette
  • aesthetic:color:avoid
  • channel:color-hue:avoid

advice

Filter the disliked yellow-green region

Remove dark yellowish-green colors when average viewer preference matters. For example, avoid hues around 85°–114° with lightness around 35–75, and penalize nearby yellow-green colors that border this region.

reason

Why this region hurts preference

A preference-driven palette can drift into disliked yellow-green colors when cool blues and strong separation are both rewarded. Filtering that region keeps the palette from solving discriminability by introducing colors that people tend to dislike.

Mechanism: Preference weighting pulls toward cool blues, and discrimination weighting can then push their opposites toward darker yellows; filtering the dark yellowish-green region blocks that undesirable path.

Evidence: The paper excludes a dark yellowish-green region because those colors are generally disliked on average, and reports that removing this region still preserved enough color space for discriminable pairings while improving typical palette preference (Gramazio et al., 2017).

Notes: The paper also adds a penalty for nearby yellow-green candidates so border cases are sampled less often.

context

Use when optimizing for average viewer preference

  • User Goal: Raise average palette appeal.
  • Task: Generate or revise a categorical palette that must balance preference with discrimination.
  • Data: Categorical groups encoded by color.
  • Chart Setting: A palette that includes blues or other colors that could otherwise drive selection toward dark yellow opposites.
  • Audience: A broad audience whose average preference matters more than individual taste.
  • Success Criterion: Fewer disliked yellow-green colors without losing usable category separation.

exceptions

Do not use when a known audience specifically wants those colors

Break it when: The design is for a known observer or audience that specifically prefers these yellow-green colors. Why: The paper notes individual differences and that some observers may like them even though they are generally disliked on average.

costs

Filtering the region reduces some available contrasts

Sacrifice: You lose part of the yellow-green color space. Risk: If applied blindly, you may remove some otherwise discriminable oppositions against blues. Mitigation: Keep using other yellow regions outside the filtered dark zone.

mistakes

Common yellow-green failure

Mistake: Letting discriminability and coolness push the palette toward dark yellow opposites of selected blues. Why it fails: That interaction produces colors the paper identifies as generally disliked.

check

Inspect the hue-lightness region directly

Failure Sign: The palette contains muddy dark yellow-greens that feel noticeably less appealing than the other colors. Quick Check: Check whether any palette color falls in the hue range 85°–114° and the lightness range 35–75. Stronger Test: Compare the current palette against a version with those colors replaced and collect preference ratings.

fix

Replace or penalize the filtered region

  • Replace colors inside the filtered region with nearby colors outside it.
  • Penalize neighboring yellow-green candidates so the palette generator samples them less often.
  • Regenerate the palette and keep the version with the higher minimum pair preference.

References

Gramazio, C. C., Laidlaw, D. H., & Schloss, K. B. (2017). Colorgorical: Creating discriminable and preferable color palettes for information visualization. IEEE Transactions on Visualization and Computer Graphics, 23(1), 521–530. https://doi.org/10.1109/TVCG.2016.2598918