Color Accessibility Guide

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:

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:

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

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