Use a diverging color scale when the data have a meaningful midpoint
For comparison in quantitative color-encoded charts, use a diverging color scale on data with a meaningful midpoint to improve insight and address one-directional magnitude readings for readers interpreting values above and below that midpoint.
- purpose:refine
- basis:heuristic
- task:compare
- data:quantitative
- quality:insight
- lever:encoding
- polish:palette
- aesthetic:color:use
advice
Center the color scale on a meaningful midpoint
Use a diverging color scale when the quantitative data have a meaningful middle value. For example, center the scale on zero, 50%, the average or median, an agreed threshold such as a poverty line, or a target such as a revenue goal.
reason
Why a meaningful midpoint helps
A diverging scale makes the middle color carry meaning instead of acting as a decorative center. Readers can then see whether values fall above or below a reference value, not just where they sit on a single low-to-high ramp.
Mechanism: The split palette turns the midpoint into an explicit benchmark and makes both sides of that benchmark visible.
Evidence: The article recommends diverging colors when there is a meaningful middle value and names zero, 50%, the average or median, an agreed threshold, and a target as valid centers (Muth, 2021).
context
Use when the midpoint is part of the message
- User Goal: Show whether values are above or below a reference value.
- Task: Compare values by side of midpoint and by distance from it.
- Data: Quantitative values with an explicit middle such as zero, 50%, median, threshold, or target.
- Chart Setting: A chart or map already using a quantitative color scale.
- Audience: Readers need to interpret the center as a real benchmark.
- Success Criterion: Readers can state what the center color means and tell which values fall on each side.
exceptions
Do not use when the middle is arbitrary
Break it when: The data do not have a meaningful middle value and are better read as a one-directional low-to-high range. Why: A diverging center would impose a split the data do not justify.
costs
Costs of adding a midpoint split
Sacrifice: You give up some immediate light-to-dark simplicity.
Risk: Readers may wonder what the middle color means if the center is weak or unexplained.
Mitigation: Use a midpoint readers can name and recognize.
mistakes
Common midpoint failure
Mistake: Centering a diverging scale on an arbitrary middle that is not a real benchmark. Why it fails: Readers infer an above-versus-below meaning that the data do not support.
check
Check that the midpoint is real
Failure Sign: Reviewers cannot explain what the middle color stands for.
Quick Check: Ask, “What exact value is the midpoint?” If it is not a named benchmark such as zero, 50%, median, threshold, or target, do not use diverging.
Stronger Test: Compare a sequential version; if the diverging center adds no interpretable above/below meaning, keep the sequential scale.
fix
Fix an arbitrary diverging center
- Set the center of the scale to an explicit benchmark.
- Replace the diverging scale with a sequential light-to-dark scale if no benchmark exists.
- Preview both sequential and diverging versions and keep the one whose midpoint has a clear meaning.