mapbox-data-visualization-patterns — Claude Code skill
Data visualization patterns for Mapbox GL JS in Claude Code, covering choropleth maps, heatmaps, and 3D styling. Helpful when plotting statistical data or point densities.
npx skills add mapbox/mapbox-agent-skills --skill mapbox-data-visualization-patterns -a claude-codeWhat mapbox-data-visualization-patterns does
This skill equips coding agents with Mapbox GL JS patterns for geographic data. It covers GeoJSON layer structures for regional choropleths, density heatmaps for event points, and 3D extrusions for terrain or building heights.
The instructions explain how to write Mapbox expressions. You get continuous gradients via interpolate and discrete buckets with step. Heatmap layers include transitions to circle points at zoom 14 so individual locations stay readable up close. Accessibility guidelines also recommend ColorBrewer palettes, cautioning against red-green pairings.
Direct GeoJSON sources work well for files under 1 MB, while larger datasets require optimization to avoid rendering lags. The patterns also show how to check for missing properties with has expressions so incomplete datasets do not crash the map style.
What it helps with
- build a choropleth map with data-driven polygon fill colors
- configure a heatmap that switches to point markers at high zoom
- pick accessible color scales that avoid red-green gradients
- guard against missing GeoJSON properties using
haschecks
Example prompts for the agent
- Create a Mapbox choropleth layer where state fill colors interpolate based on population
- Set up an incident heatmap in Mapbox that transitions to circle markers at zoom 14
- Add a discrete step color scale and error handling for missing properties in Mapbox GL
How to install mapbox-data-visualization-patterns
1Skills CLI (Vercel)
npx skills add mapbox/mapbox-agent-skills --skill mapbox-data-visualization-patterns -a claude-code2Manually: put the files in place
~/.claude/skills/mapbox-data-visualization-patterns/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/mapbox-data-visualization-patterns/
1Skills CLI (Vercel)
npx skills add mapbox/mapbox-agent-skills --skill mapbox-data-visualization-patterns -a codex2Manually: put the files in place
.agents/skills/mapbox-data-visualization-patterns/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add mapbox/mapbox-agent-skills --skill mapbox-data-visualization-patterns -a cursor2Manually: put the files in place
.cursor/skills/mapbox-data-visualization-patterns/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install mapbox/mapbox-agent-skills mapbox-data-visualization-patterns2Manually: put the files in place
.github/skills/mapbox-data-visualization-patterns/SKILL.md1Skills CLI (Vercel)
npx skills add mapbox/mapbox-agent-skills --skill mapbox-data-visualization-patterns -a gemini-cli2Platform CLI
gemini skills install https://github.com/mapbox/mapbox-agent-skills --path skills/mapbox-data-visualization-patternsManually: put the files in place
.devin/skills/mapbox-data-visualization-patterns/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add mapbox/mapbox-agent-skills --skill mapbox-data-visualization-patterns -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: How to install skills in Claude Code: five ways
Questions about mapbox-data-visualization-patterns
How do I install mapbox-data-visualization-patterns in Claude Code?
Run npx skills add mapbox/mapbox-agent-skills --skill mapbox-data-visualization-patterns -a claude-code in your terminal.
What does mapbox-data-visualization-patterns do?
It provides Mapbox GL JS templates for choropleth layers, heatmaps, 3D visualizations, accessible color palettes, and data-driven style expressions.
When should you use step instead of interpolate in Mapbox?
Use interpolate for continuous color transitions across a spectrum, and use step when you need discrete buckets or exact boundary thresholds.
Source text
SKILL.md · MIT
---
name: mapbox-data-visualization-patterns
description: Patterns for visualizing data on maps including choropleth maps, heat maps, 3D visualizations, data-driven styling, and animated data. Covers layer types, color scales, and performance optimization.
---
# Data Visualization Patterns Skill
Comprehensive patterns for visualizing data on Mapbox maps. Covers choropleth maps, heat maps, 3D extrusions, data-driven styling, animated visualizations, and performance optimization for data-heavy applications.
## When to Use This Skill
Use this skill when:
- Visualizing statistical data on maps (population, sales, demographics)
- Creating choropleth maps with color-coded regions
- Building heat maps or clustering for density visualization
- Adding 3D visualizations (building heights, terrain elevation)
- Implementing data-driven styling based on properties
- Animating time-series data
- Working with large datasets that require optimization
## Visualization Types
### Choropleth Maps
**Best for:** Regional data (states, counties, zip codes), statistical comparisons
**Pattern:** Color-code polygons based on data values
```javascript
map.on('load', () => {
// Add data source (GeoJSON with properties)
map.addSource('states', {
type: 'geojson',
data: 'https://example.com/states.geojson' // Features with population property
});
// Add fill layer with data-driven color
map.addLayer({
id: 'states-layer',
type: 'fill',
source: 'states',
paint: {
'fill-color': [
'interpolate',
['linear'],
['get', 'population'],
0,
'#f0f9ff', // Light blue for low population
500000,
'#7fcdff',
1000000,
'#0080ff',
5000000,
'#0040bf', // Dark blue for high population
10000000,
'#001f5c'
],
'fill-opacity': 0.75
}
});
// Add border layer
map.addLayer({
id: 'states-border',
type: 'line',
source: 'states',
paint: {
'line-color': '#ffffff',
'line-width': 1
}
});
// Add hover effect with reusable popup
const popup = new mapboxgl.Popup({
closeButton: false,
closeOnClick: false
});
map.on('mousemove', 'states-layer', (e) => {
if (e.features.length > 0) {
map.getCanvas().style.cursor = 'pointer';
const feature = e.features[0];
popup
.setLngLat(e.lngLat)
.setHTML(
`
<h3>${feature.properties.name}</h3>
<p>Population: ${feature.properties.population.toLocaleString()}</p>
`
)
.addTo(map);
}
});
map.on('mouseleave', 'states-layer', () => {
map.getCanvas().style.cursor = '';
popup.remove();
});
});
```
> **`step` vs `interpolate`:** The example above uses `interpolate` for smooth color gradients. For **discrete color buckets** (e.g., "low / medium / high"), use `['step', ['get', 'population'], '#f0f0f0', 500000, '#fee0d2', 2000000, '#fc9272', 10000000, '#de2d26']` instead. Prefer `step` when data has natural categories or when exact boundary values matter.
**Color Scale Strategies:**
```javascript
// Linear interpolation (continuous scale)
'fill-color': [
'interpolate',
['linear'],
['get', 'value'],
0, '#ffffcc',
25, '#78c679',
50, '#31a354',
100, '#006837'
]
// Step intervals (discrete buckets)
'fill-color': [
'step',
['get', 'value'],
'#ffffcc', // Default color
25, '#c7e9b4',
50, '#7fcdbb',
75, '#41b6c4',
100, '#2c7fb8'
]
// Case-based (categorical data)
'fill-color': [
'match',
['get', 'category'],
'residential', '#ffd700',
'commercial', '#ff6b6b',
'industrial', '#4ecdc4',
'park', '#45b7d1',
'#cccccc' // Default
]
```
### Heat Maps
**Best for:** Point density, event locations, incident clustering
**Pattern:** Visualize density of points
```javascript
map.on('load', () => {
// Add data source (points)
map.addSource('incidents', {
type: 'geojson',
data: {
type: 'FeatureCollection',
features: [
{
type: 'Feature',
geometry: {
type: 'Point',
coordinates: [-122.4194, 37.7749]
},
properties: {
intensity: 1
}
}
// ... more points
]
}
});
// Add heatmap layer
map.addLayer({
id: 'incidents-heat',
type: 'heatmap',
source: 'incidents',
maxzoom: 15,
paint: {
// Increase weight based on intensity property
'heatmap-weight': ['interpolate', ['linear'], ['get', 'intensity'], 0, 0, 6, 1],
// Increase intensity as zoom level increases
'heatmap-intensity': ['interpolate', ['linear'], ['zoom'], 0, 1, 15, 3],
// Color ramp for heatmap
'heatmap-color': [
'interpolate',
['linear'],
['heatmap-density'],
0,
'rgba(33,102,172,0)',
0.2,
'rgb(103,169,207)',
0.4,
'rgb(209,229,240)',
0.6,
'rgb(253,219,199)',
0.8,
'rgb(239,138,98)',
1,
'rgb(178,24,43)'
],
// Adjust radius by zoom level
'heatmap-radius': ['interpolate', ['linear'], ['zoom'], 0, 2, 15, 20],
// Decrease opacity at higher zoom levels
'heatmap-opacity': ['interpolate', ['linear'], ['zoom'], 7, 1, 15, 0]
}
});
// Add circle layer for individual points at high zoom
map.addLayer({
id: 'incidents-point',
type: 'circle',
source: 'incidents',
minzoom: 14,
paint: {
'circle-radius': ['interpolate', ['linear'], ['zoom'], 14, 4, 22, 30],
'circle-color': '#ff4444',
'circle-opacity': 0.8,
'circle-stroke-color': '#fff',
'circle-stroke-width': 1
}
});
});
```
## Best Practices
### Color Accessibility
```javascript
// Use ColorBrewer scales for accessibility
// https://colorbrewer2.org/
// Good: Sequential (single hue)
const sequentialScale = ['#f0f9ff', '#bae4ff', '#7fcdff', '#0080ff', '#001f5c'];
// Good: Diverging (two hues)
const divergingScale = ['#d73027', '#fc8d59', '#fee08b', '#d9ef8b', '#91cf60', '#1a9850'];
// Good: Qualitative (distinct categories)
const qualitativeScale = ['#e41a1c', '#377eb8', '#4daf4a', '#984ea3', '#ff7f00'];
// Avoid: Red-green for color-blind accessibility
// Use: Blue-orange or purple-green instead
```
### Error Handling
```javascript
// Handle missing or invalid data
map.on('load', () => {
map.addSource('data', {
type: 'geojson',
data: dataUrl
});
map.addLayer({
id: 'data-viz',
type: 'fill',
source: 'data',
paint: {
'fill-color': [
'case',
['has', 'value'], // Check if property exists
['interpolate', ['linear'], ['get', 'value'], 0, '#f0f0f0', 100, '#0080ff'],
'#cccccc' // Default color for missing data
]
}
});
// Handle map errors
map.on('error', (e) => {
console.error('Map error:', e.error);
});
});
```
## Data Size Rule
- **< 1 MB**: Use GeoJSON directly
- **1–10 MB**: Consider either GeoJSON or vector tiles depending on complexity
- **> 10 MB**: Use vector tiles (upload to Mapbox as tileset)
See [references/performance.md](references/performance.md) for implementation details.
## Reference Files
For additional visualization patterns, load the relevant reference file:
- **[references/clustering.md](references/clustering.md)** — Point clustering, custom cluster properties, clustering vs heatmap comparison
- **[references/3d-extrusions.md](references/3d-extrusions.md)** — 3D building extrusions, custom data sources, data-driven heights
- **[references/circles-lines.md](references/circles-lines.md)** — Circle/bubble maps, line data visualization, traffic flow styling
- **[references/animation.md](references/animation.md)** — Time-series animation, real-time data updates, smooth transitions
- **[references/performance.md](references/performance.md)** — Vector tiles vs GeoJSON, feature state, filtering, progressive loading
- **[references/legends-use-cases.md](references/legends-use-cases.md)** — Legend UI, data inspector, data preprocessing, election/COVID/real-estate examples
## Resources
- [Mapbox Expression Reference](https://docs.mapbox.com/style-spec/reference/expressions/)
- [ColorBrewer](https://colorbrewer2.org/) - Color scales for maps
- [Turf.js](https://turfjs.org/) - Spatial analysis
- [Simple Statistics](https://simple-statistics.github.io/) - Data classification
- [Data Visualization Tutorials](https://docs.mapbox.com/help/tutorials/#data-visualization)
© mapbox · MIT · mapbox/mapbox-agent-skills