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.
npx skills add https://github.com/mapbox/mapbox-agent-skills --skill mapbox-data-visualization-patterns
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.
Use this skill when:
Best for: Regional data (states, counties, zip codes), statistical comparisons
Pattern: Color-code polygons based on data values
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:
// 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
]
Best for: Point density, event locations, incident clustering
Pattern: Visualize density of points
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
}
});
});
// 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
// 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);
});
});
See references/performance.md for implementation details.
For additional visualization patterns, load the relevant reference file:
Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.
Populates investment banking pitch deck templates with data from source files. Use when: user provides a PowerPoint template to fill in, user has source data (Excel/CSV) to populate into slides, user mentions populating or filling a pitch deck template, or user needs to transfer data into existing slide layouts. Not for creating presentations from scratch.
Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.
Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.
Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization.
Transform text-heavy slides into visual storytelling. Suggest layout improvements, icon usage, and data visualization.
Aggregate and rank signals from multiple edge-finding skills (edge-candidate-agent, theme-detector, sector-analyst, institutional-flow-tracker) into a prioritized conviction dashboard with weighted scoring, deduplication, and contradiction detection.
Optimize Core Web Vitals (LCP, INP, CLS) for better page experience and search ranking. Use when asked to "improve Core Web Vitals", "fix LCP", "reduce CLS", "optimize INP", "page experience optimization", or "fix layout shifts". Focuses specifically on the three Core Web Vitals metrics. Do NOT use for general web performance (use perf-web-optimization), Lighthouse audits (use perf-lighthouse), or Astro-specific optimization (use perf-astro).
Take mapbox/mapbox-data-visualization-patterns from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.