mapbox-data-visualization-patterns
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.
Works with
---
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.
license: MIT
---
# 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)More Performance skills
seo-audit
coreyhaines31/marketingskills
When the user wants to audit, review, or diagnose SEO issues on their site. Also use when the user mentions "SEO audit," "technical SEO," "why am I not ranking," "SEO issues," "on-page SEO," "meta tags review," "SEO health check," "my traffic dropped," "lost rankings," "not showing up in Google," "site isn't ranking," "Google update hit me," "page speed," "core web vitals," "crawl errors," or "indexing issues." Use this even if the user just says something vague like "my SEO is bad" or "help with SEO" — start with an audit. For building pages at scale to target keywords, see programmatic-seo. For adding structured data, see schema. For AI search optimization, see ai-seo.
competitor-profiling
coreyhaines31/marketingskills
When the user wants to research, profile, or analyze competitors from their URLs. Also use when the user mentions 'competitor profile,' 'competitor research,' 'competitor analysis,' 'profile this competitor,' 'analyze competitor,' 'competitive intelligence,' 'competitor deep dive,' 'who are my competitors,' 'competitor landscape,' 'competitor dossier,' 'competitive audit,' or 'research these competitors.' Input is a list of competitor URLs. Output is structured competitor profile markdown files. For creating comparison/alternative pages from profiles, see competitors. For sales-specific battle cards, see sales-enablement.
vercel-optimize
vercel-labs/agent-skills
Use for Vercel cost and performance optimization on deployed projects, especially Next.js, SvelteKit, Nuxt, and limited Astro apps. Collect Vercel metrics, usage, project config, and code scan results first; investigate only metric-backed candidates; produce ranked recommendations grounded in verified files and version-aware Vercel/framework docs. Trigger for Vercel bill reduction, slow or expensive routes, caching opportunities, Function Invocations, Build Minutes, Fast Data Transfer, Core Web Vitals, Bot Management, Fluid compute, or cost breakdown requests.

