Designing Accessible Maps for Color Vision Deficiencies (Colorblind-Safe Cartography)

Introduction: Why accessible cartography matters

Color is one of the most powerful visual variables in cartography. It helps readers distinguish geographic features, interpret quantitative patterns, identify boundaries, and recognize spatial relationships. However, a map that works well for a cartographer with typical color vision may be difficult to interpret for someone with a color vision deficiency (CVD).

Colorblind Safe Cartography Accessible Map Design is the practice of selecting colors, symbols, visual hierarchies, and labeling strategies that communicate geographic information to the widest possible audience. It does not mean eliminating color from maps. Instead, it means ensuring that essential information remains understandable when certain colors are difficult to distinguish.

Color vision deficiencies affect how people perceive differences between colors, especially in combinations involving red, green, and sometimes blue. These differences can make thematic maps, transportation networks, hazard maps, land-cover classifications, and spatial dashboards harder to interpret.

Accessible cartography is therefore both a design responsibility and a quality-control requirement. A map should communicate its intended message even when a reader cannot distinguish every hue in its legend.

1. Understanding color vision deficiencies

Color vision deficiency is not one uniform condition. Different types affect different parts of color perception, so a palette that works well for one viewer may still create problems for another.

1.1 The three major types

The distinction between anopia and anomaly matters: anopia describes the absence of a cone class, while anomaly describes an altered response. Most people with color vision deficiency have an anomalous form rather than a complete absence of one cone class.

These categories also do not capture every individual experience. Brightness, display quality, ambient lighting, and the size of map symbols influence readability.

1.2 Why these differences matter on maps

Color can encode several kinds of geographic information:

• Ordered quantities: population density, rainfall, elevation, disease rates, or pollution concentration.

• Categories: land use, vegetation type, administrative regions, or soil classes.

• Diverging values: temperature anomalies, changes from a baseline, or positive and negative economic growth.

• Network classes: highways, railways, public transport routes, or utility infrastructure.

• Risk classes: low, moderate, high, and extreme flood or landslide hazards.

If two classes rely on colors that look similar to a reader, the map can communicate a false pattern. A high-risk area might appear indistinguishable from a low-risk area, or two land-cover types may seem to form one continuous category.

Design principle: Never make color the only visual cue for information that readers must distinguish to understand or use the map.

2. Common cartographic errors

2.1 Using red–green ramps for quantitative maps

One of the most frequent accessibility problems is using a red-to-green color ramp to represent a numerical progression.

For example, a choropleth map might use green for low unemployment and red for high unemployment. Although this convention can be familiar to some audiences, the two ends of the ramp may be difficult to distinguish for readers with protanopia or deuteranopia.

The problem becomes more severe when:

• Intermediate classes are also distinguished mainly by hue.

• Neighboring geographic units have similar brightness.

• The map is printed in grayscale or photocopied.

• Thin boundaries obscure differences between adjacent classes.

• A small legend swatch looks different from a large area of the same color.

Better approach: Use a sequential lightness-based ramp for quantities that increase in one direction, and a carefully selected diverging ramp for values that depart from a meaningful midpoint.

For example, a population-density map can progress from pale cream through blue to dark navy, while a temperature-anomaly map can use dark blue for negative values, a light neutral for zero, and dark orange for positive values.

2.2 Choosing the wrong type of color ramp

The color scheme must reflect the structure of the data, not merely the cartographer’s aesthetic preference.

A common mistake is to apply a rainbow palette to every raster. Rainbow ramps often have abrupt changes in perceived brightness and uneven visual emphasis. Some adjacent colors become difficult to distinguish, while others appear more prominent than their numerical differences justify.

2.3 Relying on color alone for critical features

A map showing flood zones in red, orange, and green may fail to communicate risk if the legend is the only place where those categories are explained.

Instead, reinforce critical distinctions with:

• Different hatch patterns for hazard zones.

• Distinct icon shapes for facilities or infrastructure.

• Direct labels on important features.

• Line styles such as solid, dashed, and dotted.

• Explicit text labels for categories that must be distinguished.

For example, a high-risk flood zone can use a dark orange fill and diagonal hatching, while a moderate-risk zone uses a lighter yellow-orange fill without hatching.

The patterns must remain visually distinct at the intended publication scale. Excessive hatching, tiny icons, and dense labels can create a new accessibility problem by making the map cluttered.

2.4 Confusing contrast with color difference

Two colors may have different hues but similar perceived lightness. Under color vision deficiency, their differences can become less apparent.

Consider a red line placed on a green polygon. A viewer may distinguish both colors under typical viewing conditions, but a viewer with red–green color vision deficiency may struggle to follow the line.

A more robust design could use a dark navy line with a light outline, making the route distinguishable through luminance contrast and edge separation rather than hue alone.

Production rule: Important boundaries, route lines, and point symbols should remain identifiable against their surrounding features even if their colors are converted to grayscale.

3. Best practices for colorblind-safe map design

3.1 Choose accessible color ramps

Several well-established palette families provide good starting points for accessible cartography.

Additional good options include selected blue–orange diverging schemes, purple–green schemes tested for the intended audience, and single-hue sequential ramps.

For categorical maps, colors such as blue, orange, purple, and selected yellow tones can provide a useful starting point. However, do not assume that any combination of these colors is automatically accessible. Check the actual shades, their lightness, and the symbol sizes at final output scale.

Resources worth keeping in a cartographer’s workflow include:

• ColorBrewer 2.0 — palette selection for thematic maps.

• Viridis color maps — documentation and comparisons for perceptually uniform color maps.

• Color Universal Design (CUD) — principles for choosing colors that remain distinguishable across different color vision conditions.

3.2 Combine hue with value and lightness

Hue describes the basic color family; lightness describes how light or dark a color appears. Saturation describes how vivid or muted it appears.

Accessible maps should not depend exclusively on hue differences.

For a sequential map, assign lighter colors to lower values and darker colors to higher values, or use the reverse when appropriate for the subject and established conventions. Keep the progression consistent across the legend and map.

For a diverging map, create two visually distinct branches around the meaningful midpoint. A dark blue–light neutral–dark orange design is often more robust than a saturated red–green design, provided the exact palette passes testing.

For categorical maps, use multiple visual variables instead of relying on subtle color differences. You can combine hue with shape, line pattern, fill pattern, or labels.

Practical test: Convert a copy of the map to grayscale. If the classes that need to be ordered become indistinguishable, strengthen the lightness contrast or introduce another visual cue. Grayscale testing is a useful diagnostic, although it is not a substitute for color vision deficiency simulation.

3.3 Use texture patterns and hatching

Patterns are especially valuable for polygon maps where neighboring areas must be distinguished, such as flood-risk zones, geological units, conservation areas, and land-use classes.

Suitable options include:

• Diagonal hatching for a designated hazard class.

• Dots for a secondary category.

• Crosshatching for overlapping designations.

• Different line patterns for transportation networks.

• Distinct point-symbol shapes for facilities or sampling locations.

Avoid assigning patterns arbitrarily. Give each pattern a consistent meaning in the legend, and do not use so many pattern types that readers must memorize a complex visual code.

For small-scale maps, dense patterns may merge into a gray texture. For large-scale maps, patterns that are too widely spaced may become visually disconnected from the polygon they represent. Always proof the patterns at final size.

3.4 Improve point, line, and boundary symbology

Accessible map design extends beyond choropleth fills.

A road network, for instance, might use a dark outer casing and a lighter inner stroke. This makes the road readable over multiple backgrounds without requiring a highly saturated color.

Point symbols should differ in shape when their categories are operationally important. A hospital can use a cross-based symbol, while a school can use a building or book symbol. Keep symbols simple enough to remain recognizable at the map’s output scale.

3.5 Design legends for independent interpretation

A legend should explain the data, not merely display colored squares.

For quantitative maps:

• Show units and class boundaries.

• Use a consistent order from low to high.

• Include a clear zero or midpoint on diverging scales.

• Avoid ambiguous labels such as “good,” “bad,” or “normal” without definitions.

For categorical maps:

• Use explicit class names.

• Pair colors with patterns or symbols where necessary.

• Keep symbol samples large enough to recognize.

• Match the legend’s appearance to the map itself.

If a map contains a color-coded risk scale, a reader should be able to identify the risk class from its label and symbology without depending on color perception alone.

4. Testing your maps for accessibility

Accessibility testing should be part of the cartographic production workflow, not a final step after the layout has been approved.

The goal is to identify features that become indistinguishable under different viewing conditions and revise them before publication.

4.1 QGIS: inspect and test map colors

QGIS provides tools for styling layers, applying color ramps, previewing layouts, and inspecting raster or vector symbology. The exact location of color vision deficiency simulation features depends on the QGIS version and installed plugins; do not assume that every installation has a built-in CVD simulation mode.

A practical workflow is:

If your workflow uses a plugin for simulation, verify its supported deficiency types and how it processes exported images. For repeatable production work, retain the original map export, simulated proofs, and a record of the revisions made.

4.2 ArcGIS Pro: refine symbology and proof the layout

In ArcGIS Pro, use the Symbology pane to select suitable color schemes and modify symbol properties. The software’s standard symbology tools are useful for creating an accessible palette, but do not assume that a particular version has a dedicated CVD simulation mode.

A practical process is:

1. Open the layer’s Symbology pane.

2. Choose a sequential, diverging, or qualitative color scheme appropriate to the data.

3. Adjust color, outline, width, and symbol shape as needed.

4. Export the map or layout to an image or PDF.

5. Open the export in a validated CVD simulation tool.

6. Review the simulated version and revise the original layer symbology.

When preparing layouts for publication, check the actual PDF or image at its intended display size. A palette that appears distinguishable on a large monitor may become ambiguous in a small report figure.

4.3 Photoshop: simulate color vision deficiencies

Adobe Photoshop includes color proofing functionality that can help identify problematic color combinations.

Depending on the version, the relevant options are available under View → Proof Setup, with options for color blindness based on protanopia and deuteranopia. Menu names and availability can vary by release.

Use the following procedure:

1. Export the map from QGIS or ArcGIS Pro as an image.

2. Open the exported map in Photoshop.

3. Select the appropriate color blindness proofing option.

4. Inspect class boundaries, line networks, symbols, and legend swatches.

5. Switch between the normal view and proof view to identify problematic distinctions.

6. Return to the original GIS project to make the corrections.

A proofing mode is a simulation, not a guarantee that every viewer will perceive the map in the same way. It also does not replace checking the contrast and clarity of the final output.

4.4 Use external simulation and contrast tools

Use one or more tools to check the exported map. If your work is safety-critical, supplement simulation with review by people with relevant visual needs wherever feasible.

4.5 What exactly should you inspect?

Do not simply ask whether the simulated map “looks good.” Evaluate specific cartographic tasks.

If a map fails any essential test, revise it and repeat the proofing process.

5. Advanced production considerations

5.1 Maintain consistency across maps and dashboards

When producing a series of maps, use a consistent visual encoding for the same variable or category. If high flood risk is represented by dark orange and diagonal hatching on one map, changing it to dark purple without explanation on another map can confuse readers.

For dashboards, test interactive states as well as static views. Hover highlights, selected polygons, toggled layers, and filtered categories must remain distinguishable when the map changes.

5.2 Consider accessibility beyond color

Colorblind-safe cartography is one part of a broader accessible design process.

• Text: Use readable type sizes and strong foreground-background contrast.

• Labels: Avoid placing essential labels over complex or similarly colored backgrounds.

• Patterns: Ensure hatching does not interfere with text or obscure boundaries.

• Interactive maps: Provide tooltips or text descriptions for important classes and symbols.

• Digital exports: Preserve sufficient resolution and avoid unnecessary transparency.

• Alternative representations: Where appropriate, provide a table or textual summary of the most important findings.

If the map is intended for a broad audience, check relevant accessibility guidance, including WCAG contrast requirements for text and applicable graphical elements. WCAG does not guarantee that a map is colorblind-safe, but its contrast principles are useful for digital cartography.

6. Summary checklist for students and cartographers

Use this checklist before submitting a GIS assignment, publishing a map, or delivering a cartographic product.

Conclusion

Accessible cartography is not about finding one perfect palette. It is about designing maps that communicate reliably across different visual abilities, display conditions, and publication formats.

The most effective workflow combines a suitable color scheme, clear lightness differences, redundant visual cues, readable legends, and systematic testing. Viridis and Magma are useful starting points for many continuous datasets, while carefully selected ColorBrewer schemes can support classified and categorical mapping.

Ultimately, color should reinforce geographic meaning, not carry that meaning alone. By incorporating accessibility checks into routine GIS production, students and professional cartographers can create maps that are clearer, more robust, and more useful to a wider audience.