[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-mapbox-mapbox-data-visualization-patterns":3,"mdc--13bclh-key":33,"related-org-mapbox-mapbox-data-visualization-patterns":6025,"related-repo-mapbox-mapbox-data-visualization-patterns":6192},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":23,"repoUrl":24,"updatedAt":25,"license":26,"forks":27,"topics":28,"repo":29,"sourceUrl":31,"mdContent":32},"mapbox-data-visualization-patterns","implement 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.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"mapbox","Mapbox","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fmapbox.png",[12,16,19,20],{"name":13,"slug":14,"type":15},"Performance","performance","tag",{"name":17,"slug":18,"type":15},"Data Visualization","data-visualization",{"name":9,"slug":8,"type":15},{"name":21,"slug":22,"type":15},"Design","design",69,"https:\u002F\u002Fgithub.com\u002Fmapbox\u002Fmapbox-agent-skills","2026-04-06T18:29:02.907655",null,10,[],{"repoUrl":24,"stars":23,"forks":27,"topics":30,"description":26},[],"https:\u002F\u002Fgithub.com\u002Fmapbox\u002Fmapbox-agent-skills\u002Ftree\u002FHEAD\u002Fskills\u002Fmapbox-data-visualization-patterns","---\nname: mapbox-data-visualization-patterns\ndescription: 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.\n---\n\n# Data Visualization Patterns Skill\n\nComprehensive 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.\n\n## When to Use This Skill\n\nUse this skill when:\n\n- Visualizing statistical data on maps (population, sales, demographics)\n- Creating choropleth maps with color-coded regions\n- Building heat maps or clustering for density visualization\n- Adding 3D visualizations (building heights, terrain elevation)\n- Implementing data-driven styling based on properties\n- Animating time-series data\n- Working with large datasets that require optimization\n\n## Visualization Types\n\n### Choropleth Maps\n\n**Best for:** Regional data (states, counties, zip codes), statistical comparisons\n\n**Pattern:** Color-code polygons based on data values\n\n```javascript\nmap.on('load', () => {\n  \u002F\u002F Add data source (GeoJSON with properties)\n  map.addSource('states', {\n    type: 'geojson',\n    data: 'https:\u002F\u002Fexample.com\u002Fstates.geojson' \u002F\u002F Features with population property\n  });\n\n  \u002F\u002F Add fill layer with data-driven color\n  map.addLayer({\n    id: 'states-layer',\n    type: 'fill',\n    source: 'states',\n    paint: {\n      'fill-color': [\n        'interpolate',\n        ['linear'],\n        ['get', 'population'],\n        0,\n        '#f0f9ff', \u002F\u002F Light blue for low population\n        500000,\n        '#7fcdff',\n        1000000,\n        '#0080ff',\n        5000000,\n        '#0040bf', \u002F\u002F Dark blue for high population\n        10000000,\n        '#001f5c'\n      ],\n      'fill-opacity': 0.75\n    }\n  });\n\n  \u002F\u002F Add border layer\n  map.addLayer({\n    id: 'states-border',\n    type: 'line',\n    source: 'states',\n    paint: {\n      'line-color': '#ffffff',\n      'line-width': 1\n    }\n  });\n\n  \u002F\u002F Add hover effect with reusable popup\n  const popup = new mapboxgl.Popup({\n    closeButton: false,\n    closeOnClick: false\n  });\n\n  map.on('mousemove', 'states-layer', (e) => {\n    if (e.features.length > 0) {\n      map.getCanvas().style.cursor = 'pointer';\n\n      const feature = e.features[0];\n      popup\n        .setLngLat(e.lngLat)\n        .setHTML(\n          `\n          \u003Ch3>${feature.properties.name}\u003C\u002Fh3>\n          \u003Cp>Population: ${feature.properties.population.toLocaleString()}\u003C\u002Fp>\n        `\n        )\n        .addTo(map);\n    }\n  });\n\n  map.on('mouseleave', 'states-layer', () => {\n    map.getCanvas().style.cursor = '';\n    popup.remove();\n  });\n});\n```\n\n> **`step` vs `interpolate`:** The example above uses `interpolate` for smooth color gradients. For **discrete color buckets** (e.g., \"low \u002F medium \u002F 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.\n\n**Color Scale Strategies:**\n\n```javascript\n\u002F\u002F Linear interpolation (continuous scale)\n'fill-color': [\n  'interpolate',\n  ['linear'],\n  ['get', 'value'],\n  0, '#ffffcc',\n  25, '#78c679',\n  50, '#31a354',\n  100, '#006837'\n]\n\n\u002F\u002F Step intervals (discrete buckets)\n'fill-color': [\n  'step',\n  ['get', 'value'],\n  '#ffffcc',  \u002F\u002F Default color\n  25, '#c7e9b4',\n  50, '#7fcdbb',\n  75, '#41b6c4',\n  100, '#2c7fb8'\n]\n\n\u002F\u002F Case-based (categorical data)\n'fill-color': [\n  'match',\n  ['get', 'category'],\n  'residential', '#ffd700',\n  'commercial', '#ff6b6b',\n  'industrial', '#4ecdc4',\n  'park', '#45b7d1',\n  '#cccccc'  \u002F\u002F Default\n]\n```\n\n### Heat Maps\n\n**Best for:** Point density, event locations, incident clustering\n\n**Pattern:** Visualize density of points\n\n```javascript\nmap.on('load', () => {\n  \u002F\u002F Add data source (points)\n  map.addSource('incidents', {\n    type: 'geojson',\n    data: {\n      type: 'FeatureCollection',\n      features: [\n        {\n          type: 'Feature',\n          geometry: {\n            type: 'Point',\n            coordinates: [-122.4194, 37.7749]\n          },\n          properties: {\n            intensity: 1\n          }\n        }\n        \u002F\u002F ... more points\n      ]\n    }\n  });\n\n  \u002F\u002F Add heatmap layer\n  map.addLayer({\n    id: 'incidents-heat',\n    type: 'heatmap',\n    source: 'incidents',\n    maxzoom: 15,\n    paint: {\n      \u002F\u002F Increase weight based on intensity property\n      'heatmap-weight': ['interpolate', ['linear'], ['get', 'intensity'], 0, 0, 6, 1],\n      \u002F\u002F Increase intensity as zoom level increases\n      'heatmap-intensity': ['interpolate', ['linear'], ['zoom'], 0, 1, 15, 3],\n      \u002F\u002F Color ramp for heatmap\n      'heatmap-color': [\n        'interpolate',\n        ['linear'],\n        ['heatmap-density'],\n        0,\n        'rgba(33,102,172,0)',\n        0.2,\n        'rgb(103,169,207)',\n        0.4,\n        'rgb(209,229,240)',\n        0.6,\n        'rgb(253,219,199)',\n        0.8,\n        'rgb(239,138,98)',\n        1,\n        'rgb(178,24,43)'\n      ],\n      \u002F\u002F Adjust radius by zoom level\n      'heatmap-radius': ['interpolate', ['linear'], ['zoom'], 0, 2, 15, 20],\n      \u002F\u002F Decrease opacity at higher zoom levels\n      'heatmap-opacity': ['interpolate', ['linear'], ['zoom'], 7, 1, 15, 0]\n    }\n  });\n\n  \u002F\u002F Add circle layer for individual points at high zoom\n  map.addLayer({\n    id: 'incidents-point',\n    type: 'circle',\n    source: 'incidents',\n    minzoom: 14,\n    paint: {\n      'circle-radius': ['interpolate', ['linear'], ['zoom'], 14, 4, 22, 30],\n      'circle-color': '#ff4444',\n      'circle-opacity': 0.8,\n      'circle-stroke-color': '#fff',\n      'circle-stroke-width': 1\n    }\n  });\n});\n```\n\n## Best Practices\n\n### Color Accessibility\n\n```javascript\n\u002F\u002F Use ColorBrewer scales for accessibility\n\u002F\u002F https:\u002F\u002Fcolorbrewer2.org\u002F\n\n\u002F\u002F Good: Sequential (single hue)\nconst sequentialScale = ['#f0f9ff', '#bae4ff', '#7fcdff', '#0080ff', '#001f5c'];\n\n\u002F\u002F Good: Diverging (two hues)\nconst divergingScale = ['#d73027', '#fc8d59', '#fee08b', '#d9ef8b', '#91cf60', '#1a9850'];\n\n\u002F\u002F Good: Qualitative (distinct categories)\nconst qualitativeScale = ['#e41a1c', '#377eb8', '#4daf4a', '#984ea3', '#ff7f00'];\n\n\u002F\u002F Avoid: Red-green for color-blind accessibility\n\u002F\u002F Use: Blue-orange or purple-green instead\n```\n\n### Error Handling\n\n```javascript\n\u002F\u002F Handle missing or invalid data\nmap.on('load', () => {\n  map.addSource('data', {\n    type: 'geojson',\n    data: dataUrl\n  });\n\n  map.addLayer({\n    id: 'data-viz',\n    type: 'fill',\n    source: 'data',\n    paint: {\n      'fill-color': [\n        'case',\n        ['has', 'value'], \u002F\u002F Check if property exists\n        ['interpolate', ['linear'], ['get', 'value'], 0, '#f0f0f0', 100, '#0080ff'],\n        '#cccccc' \u002F\u002F Default color for missing data\n      ]\n    }\n  });\n\n  \u002F\u002F Handle map errors\n  map.on('error', (e) => {\n    console.error('Map error:', e.error);\n  });\n});\n```\n\n## Data Size Rule\n\n- **\u003C 1 MB**: Use GeoJSON directly\n- **1–10 MB**: Consider either GeoJSON or vector tiles depending on complexity\n- **> 10 MB**: Use vector tiles (upload to Mapbox as tileset)\n\nSee [references\u002Fperformance.md](references\u002Fperformance.md) for implementation details.\n\n## Reference Files\n\nFor additional visualization patterns, load the relevant reference file:\n\n- **[references\u002Fclustering.md](references\u002Fclustering.md)** — Point clustering, custom cluster properties, clustering vs heatmap comparison\n- **[references\u002F3d-extrusions.md](references\u002F3d-extrusions.md)** — 3D building extrusions, custom data sources, data-driven heights\n- **[references\u002Fcircles-lines.md](references\u002Fcircles-lines.md)** — Circle\u002Fbubble maps, line data visualization, traffic flow styling\n- **[references\u002Fanimation.md](references\u002Fanimation.md)** — Time-series animation, real-time data updates, smooth transitions\n- **[references\u002Fperformance.md](references\u002Fperformance.md)** — Vector tiles vs GeoJSON, feature state, filtering, progressive loading\n- **[references\u002Flegends-use-cases.md](references\u002Flegends-use-cases.md)** — Legend UI, data inspector, data preprocessing, election\u002FCOVID\u002Freal-estate examples\n\n## Resources\n\n- [Mapbox Expression Reference](https:\u002F\u002Fdocs.mapbox.com\u002Fstyle-spec\u002Freference\u002Fexpressions\u002F)\n- [ColorBrewer](https:\u002F\u002Fcolorbrewer2.org\u002F) - Color scales for maps\n- [Turf.js](https:\u002F\u002Fturfjs.org\u002F) - Spatial analysis\n- [Simple Statistics](https:\u002F\u002Fsimple-statistics.github.io\u002F) - Data classification\n- [Data Visualization Tutorials](https:\u002F\u002Fdocs.mapbox.com\u002Fhelp\u002Ftutorials\u002F#data-visualization)\n",{"data":34,"body":35},{"name":4,"description":6},{"type":36,"children":37},"root",[38,47,53,60,65,105,111,118,129,139,1903,1958,1966,2723,2729,2738,2747,4633,4639,4645,5078,5084,5811,5817,5850,5863,5869,5874,5954,5960,6020],{"type":39,"tag":40,"props":41,"children":43},"element","h1",{"id":42},"data-visualization-patterns-skill",[44],{"type":45,"value":46},"text","Data Visualization Patterns Skill",{"type":39,"tag":48,"props":49,"children":50},"p",{},[51],{"type":45,"value":52},"Comprehensive patterns for visualizing data on Mapbox maps. 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