Map Color Palettes for Cartographic Design

Wrong colors on a choropleth map don’t just look bad, they actively lie to your audience. Map color palettes determine whether someone reads your data correctly or walks away with the opposite conclusion.

Cartographers have spent decades testing which color schemes actually work for geographic visualization. Some palettes maximize accessibility for colorblind viewers, others optimize for print reproduction, and a few sacrifice everything for visual drama.

This guide breaks down 15 battle-tested palettes from ColorBrewer, matplotlib, and traditional topographic design. You’ll learn which schemes work for sequential data like elevation, when to use diverging colors for temperature anomalies, and why qualitative palettes fail at showing continuous data visualization patterns.

Each palette includes exact hex codes, accessibility ratings, and specific use cases so you can stop guessing and start mapping with confidence.

Map Color Palettes

Viridis

Perceptually uniform sequential palette designed for scientific visualization. Dark purple transitions through blue and green to bright yellow, maintaining consistent luminance changes across the entire spectrum.

Color Composition

Ten-color progression from #440154 (deep purple) to #FDE724 (vibrant yellow). Built on perceptually uniform color theory principles with linear luminance increases.

Each step represents equal perceived difference in brightness and hue.

Best Use Cases

Elevation maps, population density, climate data, heatmaps. Works for any continuous numerical data requiring clear gradient visualization.

Visual Accessibility

Designed specifically for colorblind users (deuteranopia, protanopia, tritanopia). Maintains high contrast ratios across all color transitions.

Readable in grayscale conversion without data loss.

Data Representation Strengths

Sequential data with clear low-to-high progression. Temperature maps, depth measurements, concentration levels benefit from uniform perceptual steps.

Zero ambiguity in value ordering due to consistent luminance gradient.

Limitations & Considerations

Not suitable for categorical data or diverging datasets with a critical midpoint. The yellow endpoint can oversaturate at high saturation levels in print.

Poor choice for maps requiring neutral tones or earth color palettes.

Blues (Sequential)

ColorBrewer’s classic monochromatic progression from near-white to deep navy. Trusted standard for water-related cartography and single-variable thematic maps.

Color Composition

Nine colors ranging from #F7FBFF (ice blue) to #08306B (midnight blue). Pure monochrome colors scheme with consistent hue, varying only in lightness.

No purple or green color palettes contamination.

Best Use Cases

Bathymetric maps, precipitation data, lake depth visualization, choropleth maps showing single variables. Standard for water body mapping.

Visual Accessibility

Excellent for deuteranopia and protanopia (red-green colorblindness). Struggles with tritanopia due to blue dependency.

Loses some distinction in grayscale but maintains relative ordering.

Data Representation Strengths

Clean sequential data representation without emotional bias. Works for demographic data, economic indicators, any single-direction progression needing professional presentation.

Limitations & Considerations

Lacks visual punch for grabbing attention in busy dashboards. Not suitable for diverging data with positive/negative split.

Avoid when warm color palettes would better match content context (temperature, fire risk).

YlOrRd (Yellow-Orange-Red)

Heat-based sequential palette progressing from pale yellow through orange to deep red. Perfect for data requiring immediate visual urgency or temperature connotations.

Color Composition

Nine colors spanning #FFFFCC (cream yellow) to #800026 (burgundy). Analogous colors progression through warm spectrum only.

Natural gradient mimics fire and heat perception.

Best Use Cases

Temperature maps, fire danger zones, heat risk areas, disease outbreak intensity, population density in urban planning. Any data where “hot” metaphor applies.

Visual Accessibility

Problematic for protanopia (red-blind users) who see compressed range at red end. Deuteranopia handles it better.

Grayscale conversion loses some differentiation in middle orange tones.

Data Representation Strengths

Intuitive “more is hotter” reading without legend reference. Strong emotional impact for conveying urgency or risk levels.

Works well with sunset color palettes for aesthetic consistency.

Limitations & Considerations

Cultural associations with danger may bias data interpretation. Not neutral enough for academic publications requiring objective presentation.

Terrible for diverging data needing cool counterpart; stick with red color palettes context only.

RdYlGn (Diverging)

Classic diverging scheme splitting negative (red) and positive (green) values around neutral yellow midpoint. Standard for data with meaningful zero or average.

Color Composition

Eleven colors with #FFFFBF neutral center flanked by red (#A50026 extreme) and green (#006837 extreme). Symmetrical balance around midpoint.

Uses complementary color scheme to maximize perceptual difference.

Best Use Cases

Election results (gains/losses), temperature anomalies (above/below average), economic growth/decline, survey sentiment (negative to positive). Any data with critical middle value.

Visual Accessibility

Major issues for red-green colorblind users (8% of males). Should never be used without alternative encoding (patterns, labels).

Deuteranopia sees two yellows instead of red/green split.

Data Representation Strengths

Immediate visual split between opposing values. The neutral yellow creates clear focal point at zero/average line.

Limitations & Considerations

Cultural red/green associations (stop/go, bad/good) introduce interpretation bias. Fails WCAG accessibility standards without additional encoding.

Never use for colorblind audiences; choose blue color palettes and orange diverging instead.

Plasma

Perceptually uniform palette with high visual impact. Runs from deep purple through magenta and orange to bright yellow.

Color Composition

Ten colors from #0D0887 (navy purple) to #F0F921 (electric yellow). Similar to Viridis but with warmer, more saturated mid-tones creating stronger visual punch.

Maintains uniform perceptual steps like all matplotlib palettes.

Best Use Cases

Scientific heatmaps requiring attention, radiation maps, energy distribution, particle physics visualization. Best for presentations needing visual drama.

Visual Accessibility

Colorblind-safe across all types. Slightly less optimal than Viridis for tritanopia but still maintains distinction.

Print reproduction excellent due to high saturation throughout range.

Data Representation Strengths

Sequential data needing more visual excitement than clinical blue-green schemes. The pink color palettes influence in mid-range adds warmth.

Limitations & Considerations

Too vibrant for conservative contexts (government reports, academic journals). The dramatic colors can distract from data in text-heavy dashboards.

Avoid when subtlety matters more than impact; use muted color palettes instead.

Greens (Sequential)

Monochromatic green progression from near-white to deep forest green. Default choice for vegetation, environmental, and positive-trending data.

Color Composition

Nine colors spanning #F7FCF5 to #00441B. Pure green hue with no yellow or blue contamination.

Consistent lightness intervals matching ColorBrewer’s Blues palette structure.

Best Use Cases

Vegetation density, forest cover, agricultural yield, environmental health indicators, parkland distribution. Natural fit for nature color palettes context.

Visual Accessibility

Problematic for deuteranopia (green-blind users). Protanopia sees it as muddy browns.

Better alternatives exist for colorblind audiences; this palette prioritizes aesthetic over accessibility.

Data Representation Strengths

Culturally positive associations (growth, health, sustainability). Works well for data meant to show improvement or ecological themes.

Matches spring color palettes for seasonal mapping.

Limitations & Considerations

Strongly biases interpretation toward “good” regardless of actual data meaning. Not neutral enough for objective scientific work.

Never use for financial losses, pollution levels, or any negative-trending data where green implies opposite meaning.

Spectral (Diverging)

Rainbow-inspired diverging palette with yellow center. Spans full visible spectrum from warm reds through cool purple blue endpoint.

Color Composition

Eleven colors from #9E0142 (crimson) through #FFFFBF (pale yellow) to #5E4FA2 (violet). Follows natural light spectrum progression.

Higher saturation than most diverging palettes creates strong visual hierarchy.

Best Use Cases

Temperature anomalies, elevation changes, correlation coefficients, survey responses ranging negative to positive. Works when rainbow aesthetics acceptable.

Visual Accessibility

Terrible for colorblind users. Red-green distinction fails completely for deuteranopia and protanopia.

Should include secondary encoding (hatching, labels) if accessibility matters at all.

Data Representation Strengths

Intuitive hot-to-cold or spectrum-like interpretation. The wide hue range maximizes visual distinction between extreme values.

Popular in climate science despite accessibility issues.

Limitations & Considerations

Rainbow palettes create false boundaries where smooth gradient color palettes transitions should exist. Perceptually non-uniform.

Criticized by cartography experts; use only when audience expects rainbow convention or accessibility isn’t required.

Paired (Qualitative)

Qualitative palette with light/dark pairs for grouped categorical data. Twelve colors organized as six related duos.

Color Composition

Six hue families each containing light and dark variant. Blue (#A6CEE3, #1F78B4), green (#B2DF8A, #33A02C), red (#FB9A99, #E31A1C), orange color palettes (#FDBF6F, #FF7F00), purple color palettes (#CAB2D6, #6A3D9A), plus yellow color palettes (#FFFF99) and brown (#B15928).

Best Use Cases

Political maps with subcategories (parties and candidates), land use with subtypes (residential subdivided into density levels), demographic groups with age brackets.

Visual Accessibility

Better than random qualitative schemes but still fails red-green distinction. The pairing structure helps by adding lightness variation.

Use patterns or labels for critical distinctions.

Data Representation Strengths

The light/dark pairing shows hierarchical relationships within categories. Main category uses dark, subcategories use light tint.

Works for up to six main categories with two subcategories each.

Limitations & Considerations

Limited to twelve colors total. Not suitable for more than six primary categories.

The yellow (#FFFF99) is nearly invisible on white backgrounds; requires borders or avoid entirely.

Inferno

Darkest of the matplotlib perceptually uniform palettes. Starts from near-black and progresses through deep purples and reds to pale yellow.

Color Composition

Ten colors from #000004 (true black) to #FCFFA4 (cream). Passes through dark purple, magenta, burnt orange, and golden tones.

Most dramatic low-end of any standard sequential palette.

Best Use Cases

Night color palettes mapping, low-light data visualization, astronomical maps, radiation intensity. Perfect when zero values need true black representation.

Volcanic activity maps benefit from the literal inferno progression.

Visual Accessibility

Colorblind-safe with excellent distinction across all deficiency types. The dark baseline provides exceptional contrast on light backgrounds.

Prints well but requires high-quality output to preserve dark purple distinctions.

Data Representation Strengths

Zero values clearly defined by black starting point. The fire-like progression intuitively reads as intensity increase.

Strong choice when dramatic dark color palettes aesthetic matches content theme.

Limitations & Considerations

Too dark for print materials with light text overlay. The black endpoint makes annotations difficult without halos.

Avoid for daytime data or content requiring cheerful tone; use bright color palettes instead.

BuPu (Blue-Purple)

Elegant transition from light blue through lavender to deep eggplant. Less common than single-hue schemes but offers sophisticated aesthetic.

Color Composition

Nine colors spanning #F7FCFD (ice blue) to #4D004B (aubergine). Smooth hue shift from cyan through periwinkle to purple.

More interesting than monochromatic while maintaining clear progression.

Best Use Cases

Demographics with “cool” associations (elderly population, winter tourism, healthcare access). Pharmaceutical distribution maps, wellness indicators, calm-themed data.

Works beautifully for pastel color palettes requirement at low end.

Visual Accessibility

Good for most colorblind types but tritanopia struggles with blue-purple distinction. Better than pure blue schemes for variety.

Grayscale conversion maintains reasonable differentiation.

Data Representation Strengths

Professional appearance without the coldness of pure blues or intensity of pure purples. Reads as trustworthy and creative simultaneously.

Matches cool color palettes aesthetic without temperature implications.

Limitations & Considerations

The hue shift can create perceived “break” at midpoint where blue becomes purple. Not ideal for data requiring perfectly smooth perception.

Avoid when color consistency across entire range matters more than visual interest.

Set1 (Qualitative)

Maximum distinction qualitative set for categorical mapping. Nine highly saturated colors optimized for quick visual separation.

Color Composition

#E41A1C (red), #377EB8 (blue), #4DAF4A (green), #984EA3 (purple), #FF7F00 (orange), #FFFF33 (yellow), #A65628 (brown), #F781BF (hot pink), #999999 (gray).

Maximum hue spacing with high saturation except gray neutral.

Best Use Cases

Political districts, land use categories, administrative boundaries, species distribution maps. Best for nine or fewer categories needing instant recognition.

Standard choice when typography won’t fit and color must carry full meaning.

Visual Accessibility

Poor red-green distinction for colorblind users. The high saturation actually worsens accessibility in some contexts.

Requires patterns, textures, or labels as secondary encoding.

Data Representation Strengths

ColorBrewer’s most recognizable palette. Familiar to scientific audiences from countless publications.

The variety in hue ensures no two categories appear related when they aren’t.

Limitations & Considerations

Limited to nine categories maximum. The yellow (#FFFF33) has terrible contrast on white backgrounds.

Not suitable for print-only materials; gray color palettes context provides only neutral option.

Cividis

Optimized specifically for colorblind viewers. Perceptually uniform across all color vision deficiencies including complete colorblindness.

Color Composition

Nine colors from #00204D (navy blue) through olive tones to #FFF94C (bright yellow). Deliberately avoids red and green entirely.

Uses blue-yellow axis only for universal accessibility.

Best Use Cases

Government publications, healthcare data, educational materials, scientific journals requiring WCAG compliance. Any context prioritizing accessibility over aesthetics.

Required for EU accessibility regulations in public sector maps.

Visual Accessibility

Best-in-class for all colorblind types. Maintains distinction in grayscale conversion better than any alternative.

The only palette meeting universal design standards without compromise.

Data Representation Strengths

Sequential data with legal accessibility requirements. Works for military, medical, and regulatory contexts where exclusion isn’t acceptable.

Prints reliably across all output methods and paper qualities.

Limitations & Considerations

Less visually appealing than Viridis or Plasma. The olive mid-tones appear muddy to normal color vision users.

Not suitable when aesthetic impact matters more than inclusion; use vivid color palettes instead.

PuBuGn (Purple-Blue-Green)

Multi-hue sequential moving from pale lilac through teal to deep teal color palettes endpoint. Sophisticated alternative to single-color schemes.

Color Composition

Nine colors spanning #FFF7FB (barely pink) to #014636 (forest teal). Smooth transition through powder blue and turquoise middle range.

More dynamic than Blues palette while remaining professional.

Best Use Cases

Water quality gradients, coastal data, marine conservation, precipitation levels. Natural fit for aquatic and environmental mapping.

Works well for sea color palettes contexts requiring depth variation.

Visual Accessibility

Reasonable for deuteranopia and protanopia. Tritanopia sees compressed range at blue-green boundary.

Better accessibility than pure green schemes but not as safe as blue-only palettes.

Data Representation Strengths

The light purple starting point provides soft entry without starkness of white. Green endpoint suggests growth or ecological health.

Combines calming blue with optimistic green associations.

Limitations & Considerations

The multi-hue transition creates subtle perceived boundaries where hue shifts. Not truly continuous for sensitive applications.

Avoid when pure blue would be more conventional (standard water body mapping).

OrRd (Orange-Red)

Warm sequential from cream through peach and coral to dark red. Slightly more orange-biased than YlOrRd.

Color Composition

Nine colors from #FFF7EC (ivory) to #7F0000 (maroon). Passes through apricot and salmon mid-tones before hitting crimson.

Maintains warmth throughout entire progression without yellow contamination.

Best Use Cases

Drought severity, heat index, wildfire risk, soil erosion. Data with intensity connotations but requiring softer entry than pure red.

Better than YlOrRd when yellow feels wrong for context.

Visual Accessibility

Problematic for protanopia (red-blind users). The orange-red distinction compresses severely.

Deuteranopia handles it slightly better but still sees diminished range.

Data Representation Strengths

The light orange beginning feels less neutral than yellow, implying warmth from start. Good for autumn color palettes themed data.

Less alarming than pure red schemes while maintaining urgency at high end.

Limitations & Considerations

Limited to warm data contexts only. Inappropriate for any “cool” associations (water depth, winter conditions).

Never use for diverging data; lacks cold counterpart within same scheme.

Terrain (Topographic)

Classic hypsometric tinting for elevation. Green lowlands progress through yellows and tans to pale peaks.

Color Composition

Ten colors from #00A600 (kelly green) to #F2F2F2 (light gray). Mimics natural landscape coloring from sea level vegetation to snow-capped mountains.

Traditional cartographic convention dating to pre-digital mapping.

Best Use Cases

Physical terrain maps, hiking trail guides, geographical atlases, topographic surveys. The standard for elevation visualization worldwide.

Instantly recognizable to anyone who’s read a physical map.

Visual Accessibility

Poor for colorblind users. Green-yellow-brown transitions compress badly for deuteranopia.

Cultural familiarity often overrides accessibility concerns in this specific use case.

Data Representation Strengths

Culturally ingrained association between colors and elevation. No legend needed for general interpretation.

The progression from fertile lowlands (green) to barren peaks (gray/white) matches real-world color distribution.

Visual Accessibility

Works with human spatial cognition expectations. We expect valleys to be green, peaks to be pale.

Limitations & Considerations

Completely inappropriate for non-elevation data despite being sequential. Using terrain colors for population density would confuse every viewer.

Never deviate from elevation context; this palette has single-purpose utility only.

FAQ on Map Color Palettes

What is the best color palette for choropleth maps?

Depends on your data type. Sequential palettes like Blues work for continuous data, diverging schemes like RdYlGn show deviations from a midpoint, and qualitative sets like Paired handle categorical data.

Choose based on what you’re measuring, not aesthetics.

How do I make maps colorblind-friendly?

Use Viridis, Cividis, or any ColorBrewer palette marked “colorblind safe.” Avoid red-green combinations entirely.

Add patterns or textures as secondary encoding. Test your map with colorblind simulation tools before publishing.

What’s the difference between sequential and diverging color schemes?

Sequential palettes progress from light to dark in one direction (Blues, Greens). Diverging schemes split around a critical midpoint with contrasting colors (RdYlGn, Spectral).

Use diverging when zero or average matters.

Why shouldn’t I use rainbow colors for maps?

Rainbow palettes create false boundaries where smooth gradient transitions should exist. They’re perceptually non-uniform and fail colorblind accessibility.

Spectral is acceptable only when rainbow convention is expected, like climate data.

How many colors should a map palette have?

Sequential data works with 5-9 colors. Qualitative categorical maps max out at 9-12 distinct colors before confusion sets in.

More colors don’t improve clarity, they reduce it by overwhelming visual hierarchy.

What are the best palettes for terrain mapping?

Traditional Terrain palette (green to brown to white) remains the standard for elevation. YlOrRd works for heat or drought severity.

BuPu handles bathymetric ocean depth effectively through blue-to-purple progression.

Can I use the same palette for print and web?

Most ColorBrewer palettes work for both, but verify CMYK conversion before printing. Viridis and Plasma maintain saturation better than pastels.

Test prints on actual paper stock: screen colors always look different.

How do I choose colors for political maps?

Use qualitative palettes like Set1 or Paired for distinct parties or districts. Maintain contrast between adjacent regions.

Never imply ranking with sequential colors when categories are equal in importance.

What’s ColorBrewer and why does everyone use it?

ColorBrewer is Cynthia Brewer’s research-based palette system optimized for cartography. It provides tested schemes for sequential, diverging, and qualitative data with built-in accessibility guidance.

Standard in GIS software like ArcGIS and QGIS.

Should I use Viridis or Plasma for heatmaps?

Viridis for scientific accuracy and maximum accessibility. Plasma when you need more visual impact and aesthetic appeal.

Both are perceptually uniform, but Plasma’s warmer tones grab attention better in presentations.

Conclusion

Choosing the right map color palettes separates professional cartography from amateur guesswork. Sequential schemes like Greens handle single-variable data, diverging palettes like Spectral show deviations, and qualitative sets manage categorical boundaries.

ColorBrewer remains the gold standard, but matplotlib’s perceptually uniform options (Viridis, Plasma, Inferno) solve accessibility challenges that traditional schemes can’t touch.

Your data type dictates palette selection. Topographic maps demand terrain colors, population density needs sequential blues or purples, and political districts require high-contrast qualitative schemes.

Test every palette for colorblind accessibility before publishing. Add typography elements like labels and legends as backup encoding.

Print reproduction differs dramatically from screen display. Verify RGB to CMYK conversion if physical output matters. The wrong palette doesn’t just look bad; it communicates false information to your audience.

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Bogdan Sandu

Bogdan Sandu is a seasoned designer who has been designing websites since 2008.

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