mapbox-data-visualization-patterns
This skill teaches the agent to implement data visualization patterns on Mapbox maps, including choropleth maps, heat maps, 3D extrusions, and animated data…
Identity proven by GitHub's verified organisation badge (mapbox)
- Agents
- Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode, claude.ai and API
- License
- MIT (permissive)
- Pin
- f71cd72pinned Oct 9, 2026
- Context cost
- about 2,100 tokenswhen the skill loads
- GitHub stars
- 80on mapbox/mapbox-agent-skills
- Scripts
- None found
What it does
“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.”
Generated summary from the skill's own text
This skill teaches the agent to implement data visualization patterns on Mapbox maps, including choropleth maps, heat maps, 3D extrusions, and animated data layers. The agent learns to use Mapbox expressions for data-driven styling, select appropriate color scales, optimize performance for large datasets, and handle missing data gracefully.
When to use it
- Displaying statistical data across regions with color-coded polygons
- Visualizing point density or event clustering on maps
- Creating 3D visualizations like building heights or terrain
- Building time-series or animated data visualizations on maps
When not to
- Projects not using Mapbox as the mapping platform
- Simple map applications without statistical or spatial data visualization
Install
These commands install from the publisher's repository and need no account.
skills CLI
npx skills add mapbox/mapbox-agent-skills --skill mapbox-data-visualization-patternsInstalls from the repository's default branch, which may have changed since the commit we checked.
GitHub CLI 2.90 or later
gh skill install mapbox/mapbox-agent-skills mapbox-data-visualization-patterns@f71cd72b8d8937b06db440538bd65559eb792c49Installs the commit we checked (f71cd72).
Copy by hand
Download the folder at f71cd72 and put it in .claude/skills/mapbox-data-visualization-patterns for Claude Code and in .agents/skills/mapbox-data-visualization-patterns for Codex, Cursor, Gemini CLI, GitHub Copilot and OpenCode.
Claude Code plugin
/plugin install mapbox@claude-plugins-officialThe publisher's plugin in Claude's official marketplace. It may hold more than this skill, and we have not pinned it.
Copy for your agent
A prompt for any coding agent. It installs this skill from GitHub at the pinned commit, copies the files as they are, and runs nothing unless you ask.
Prompt
Install the agent skill "mapbox-data-visualization-patterns" by Mapbox. Source, pinned to commit f71cd72b8d8937b06db440538bd65559eb792c49: https://github.com/mapbox/mapbox-agent-skills/tree/f71cd72b8d8937b06db440538bd65559eb792c49/skills/mapbox-data-visualization-patterns 1. Download that folder at exactly this commit. Do not use the default branch. 2. Copy every file unchanged, keeping the folder structure, into .claude/skills/mapbox-data-visualization-patterns/ for Claude Code and .agents/skills/mapbox-data-visualization-patterns/ for Codex, Cursor, Gemini CLI, GitHub Copilot and OpenCode. Create the folders if they do not exist. 3. Do not edit the files, and do not run any script in the folder unless I ask you to. 4. When you are done, list the files you copied and where you put them. Listing and checks: https://www.agentnative.dev/skills/mapbox/mapbox-data-visualization-patterns
SKILL.md at the pin
---
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)
From mapbox/mapbox-agent-skills at f71cd72, shown as plain text under its license: MIT (permissive). The license text is in LICENSE.md.
View SKILL.md on GitHub at f71cd72Files
- SKILL.md8.6 KB · 9a34708ae015
- AGENTS.md13 KB · 942f39748259
- evals/
- evals.json2.4 KB · ce08f0cda1e5
- references/
- 3d-extrusions.md3.0 KB · 7e36a3d3e736
- animation.md2.8 KB · 666ef212dc47
- circles-lines.md2.4 KB · 00e080e1faad
- clustering.md3.9 KB · b0207f36a8a0
- legends-use-cases.md4.5 KB · 0771dba3fa3c
- performance.md4.3 KB · 2bdf0567c9f1
9 files, 44 KB in total. Each file has a sha256 in the pin's manifest; the first 12 characters are shown.
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