What is the difference between sequential and diverging maps?

A sequential map runs light to dark in a single hue for ordered data like population. A diverging map has two contrasting hues meeting at a neutral midpoint, which suits data that deviates above and below a reference value such as temperature anomaly.

Why interpolate in a perceptual space rather than plain RGB?

Linear RGB interpolation produces dull or muddy mid-tones and uneven perceived steps. This tool interpolates in the CIELAB space, which is closer to how the eye perceives lightness and color difference, giving more even-looking ramps like ColorBrewer.

How many classes should I use?

For choropleth maps five to seven classes are common because the eye struggles to distinguish more shades reliably. Use fewer classes for print and more only for continuous color scales rendered by software.

Are these palettes colorblind-safe?

Sequential single-hue ramps are generally safe. For diverging maps avoid red-green endpoints; blue-orange or purple-green pairs are far more distinguishable for the most common color vision deficiencies.

Can I use these for continuous gradients?

Yes. Use a high class count, or take the two endpoints and let your library interpolate. The hex stops produced here define the control points of the ramp.

Gradient Color Map Generator

Good data visualization depends on color maps that step evenly to the eye and encode the structure of the data. This generator builds sequential and diverging color maps by interpolating between endpoints in the perceptual CIELAB space — the same principle behind well-regarded schemes like ColorBrewer — and outputs a clean hex array.

How it works

Each endpoint color is converted from sRGB to CIELAB. The tool then interpolates each L*, a*, and b* channel linearly across the requested number of classes and converts every stop back to sRGB hex. Interpolating in LAB rather than RGB keeps the perceived lightness changing smoothly:

for i in 0..n-1:
  t = i / (n - 1)
  lab = lerp(startLab, endLab, t)   # sequential
  rgb = labToRgb(lab)

For a diverging map the ramp is built in two halves — start to midpoint, then midpoint to end — so the neutral color sits exactly in the center class.

Example and tips

A sequential blue map from a pale #f7fbff to a deep #08306b over six classes gives evenly spaced shades ideal for a population choropleth. For a diverging map representing change, use orange-to-grey-to-blue rather than red-to-green so the result stays readable for colorblind viewers. Keep classes between five and seven for maps; reserve high counts for software-rendered continuous scales.