Color Accessibility Guide

Design data visualizations that are accessible to colorblind readers and follow best practices for clarity, using tested palettes and encoding principles.

Color Vision Deficiency Overview

Approximately 8% of males and 0.5% of females have some form of color vision deficiency (CVD). The most common types:

Type	Prevalence (Male)	Affected Colors	Commonly Confused
Deuteranomaly (green-weak)	5%	Green	Red and green
Protanomaly (red-weak)	1%	Red	Red and green
Deuteranopia (no green)	1%	Green	Red and green
Protanopia (no red)	1%	Red	Red and green
Tritanopia (no blue)	0.003%	Blue	Blue and yellow
Monochromacy	Very rare	All	All colors

Key takeaway: Never rely solely on a red-green distinction to convey information. About 1 in 12 male readers cannot distinguish them.

Recommended Colorblind-Safe Palettes

Qualitative Palettes (Categorical Data)

Wong (2011) Nature Palette (8 colors)

Widely recommended for scientific publications:

# Wong's colorblind-friendly palette
wong_palette = {
    "black":       "#000000",
    "orange":      "#E69F00",
    "sky_blue":    "#56B4E9",
    "bluish_green":"#009E73",
    "yellow":      "#F0E442",
    "blue":        "#0072B2",
    "vermillion":  "#D55E00",
    "reddish_purple":"#CC79A7"
}

Okabe-Ito Palette

okabe_ito = ["#E69F00", "#56B4E9", "#009E73", "#F0E442",
             "#0072B2", "#D55E00", "#CC79A7", "#000000"]

Tol's Qualitative Palette

# Paul Tol's qualitative palette (up to 12 distinct colors)
tol_qualitative = ["#332288", "#88CCEE", "#44AA99", "#117733",
                   "#999933", "#DDCC77", "#CC6677", "#882255",
                   "#AA4499", "#661100", "#6699CC", "#888888"]

Sequential Palettes (Ordered Data)

For continuous data, use perceptually uniform colormaps:

import matplotlib.pyplot as plt

# Recommended sequential colormaps
# These are perceptually uniform and colorblind-safe:
good_cmaps = ["viridis", "plasma", "inferno", "magma", "cividis"]

# Avoid these (not perceptually uniform, not colorblind-safe):
bad_cmaps = ["jet", "rainbow", "hsv"]  # NEVER use these

# Example usage
import numpy as np
data = np.random.randn(10, 10)
fig, ax = plt.subplots(figsize=(8, 6))
im = ax.imshow(data, cmap="viridis")
plt.colorbar(im)
plt.title("Use viridis, not jet")
plt.savefig("heatmap.pdf", dpi=300, bbox_inches="tight")

Diverging Palettes (Data with Meaningful Center)

# Colorblind-safe diverging palettes
# Blue-to-Red via white (good for temperature, correlation)
import matplotlib.colors as mcolors

# Built-in matplotlib options:
diverging_safe = ["RdBu_r", "PuOr_r", "BrBG"]

# Custom two-color diverging (Tol):
tol_diverging = ["#364B9A", "#4A7BB7", "#6EA6CD", "#98CAE1", "#C2E4EF",
                 "#EAECCC", "#FEDA8B", "#FDB366", "#F67E4B", "#DD3D2D", "#A50026"]

Design Principles for Accessible Visualization

1. Data-Ink Ratio

Edward Tufte's principle: maximize the proportion of ink used to display actual data.

import matplotlib.pyplot as plt

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))

# BAD: Low data-ink ratio (chartjunk)
ax1.bar(range(5), [3, 7, 2, 5, 8], color="blue", edgecolor="black",
        linewidth=2)
ax1.set_facecolor("#EEEEEE")
ax1.grid(True, color="white", linewidth=2)
ax1.set_title("Before: Low Data-Ink Ratio")

# GOOD: High data-ink ratio
ax2.bar(range(5), [3, 7, 2, 5, 8], color="#0072B2", edgecolor="none")
ax2.spines["top"].set_visible(False)
ax2.spines["right"].set_visible(False)
ax2.set_title("After: High Data-Ink Ratio")

plt.tight_layout()
plt.savefig("data_ink_ratio.pdf", dpi=300)

2. Redundant Encoding

Never use color as the sole channel for conveying information. Combine color with at least one other visual channel:

Channel	Examples
Shape	Circles, squares, triangles for different groups
Pattern	Solid, dashed, dotted lines
Fill pattern	Hatching, cross-hatching for bar charts
Label	Direct text labels on or near data points
Position	Separate panels (facets) for each group
Size	Varying point sizes

import matplotlib.pyplot as plt

markers = ['o', 's', '^', 'D']  # Different shapes
colors = ['#0072B2', '#D55E00', '#009E73', '#CC79A7']
labels = ['Group A', 'Group B', 'Group C', 'Group D']

fig, ax = plt.subplots(figsize=(8, 6))
for i in range(4):
    ax.scatter(x[i], y[i], c=colors[i], marker=markers[i],
               s=80, label=labels[i], edgecolors='black', linewidth=0.5)

ax.legend()
ax.set_xlabel("X Variable")
ax.set_ylabel("Y Variable")
plt.savefig("redundant_encoding.pdf", dpi=300)

3. Line Style Differentiation

line_styles = ['-', '--', '-.', ':', (0, (3, 1, 1, 1))]
colors = ['#0072B2', '#D55E00', '#009E73', '#CC79A7', '#E69F00']

fig, ax = plt.subplots(figsize=(8, 5))
for i in range(5):
    ax.plot(x, data[i], color=colors[i], linestyle=line_styles[i],
            linewidth=2, label=f"Method {i+1}")

ax.legend()

Checking Your Visualizations

Simulation Tools

Tool	Platform	URL
Coblis	Web	color-blindness.com/coblis
Color Oracle	Desktop (Win/Mac/Linux)	colororacle.org
Sim Daltonism	macOS	michelf.ca/projects/sim-daltonism
Colorblindly	Chrome extension	Chrome Web Store
Matplotlib CVD simulation	Python	See code below

Programmatic CVD Simulation

from colorspacious import cspace_convert
import numpy as np

def simulate_cvd(rgb_hex, deficiency="deuteranomaly", severity=100):
    """Simulate how a color appears to someone with CVD."""
    # Convert hex to RGB [0,1]
    rgb = np.array([int(rgb_hex[i:i+2], 16)/255 for i in (1, 3, 5)])

    # Convert using colorspacious
    cvd_space = {"name": "sRGB1+CVD",
                 "cvd_type": deficiency,
                 "severity": severity}
    rgb_cvd = cspace_convert(rgb, cvd_space, "sRGB1")
    rgb_cvd = np.clip(rgb_cvd, 0, 1)

    return "#{:02x}{:02x}{:02x}".format(*[int(c*255) for c in rgb_cvd])

# Test your palette
for color in ["#FF0000", "#00FF00", "#0072B2", "#D55E00"]:
    sim = simulate_cvd(color)
    print(f"{color} -> {sim} (deuteranomaly)")

Quick Reference: Do's and Don'ts

Do	Don't
Use Wong or Okabe-Ito palettes	Use red vs. green to distinguish categories
Use viridis/cividis colormaps	Use jet/rainbow colormaps
Add shape/pattern as redundant encoding	Rely on color alone
Use direct labels when possible	Force readers to match colors to legend repeatedly
Test with CVD simulators	Assume your color choices work for everyone
Use high contrast (WCAG AA: 4.5:1 ratio)	Use light colors on white backgrounds
Keep maximum 7-8 colors in categorical charts	Use 15+ colors that are impossible to distinguish