mapbox-data-visualization-patterns

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

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
]

Heat Maps

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'], , , , ],
      
      : [
        ,
        [],
        [],
        ,
        ,
        ,
        ,
        ,
        ,
        ,
        ,
        ,
        ,
        ,
        
      ],
      
      : [, [], [], , , , ],
      
      : [, [], [], , , , ]
    }
  });

  
  map.({
    : ,
    : ,
    : ,
    : ,
    : {
      : [, [], [], , , , ],
      : ,
      : ,
      : ,
      : 
    }
  });
});

Best Practices

Color Accessibility

// 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

// 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 for implementation details.

Reference Files

For additional visualization patterns, load the relevant reference file:

• references/clustering.md — Point clustering, custom cluster properties, clustering vs heatmap comparison
• references/3d-extrusions.md — 3D building extrusions, custom data sources, data-driven heights
• references/circles-lines.md — Circle/bubble maps, line data visualization, traffic flow styling
• references/animation.md — Time-series animation, real-time data updates, smooth transitions
• references/performance.md — Vector tiles vs GeoJSON, feature state, filtering, progressive loading
• references/legends-use-cases.md — Legend UI, data inspector, data preprocessing, election/COVID/real-estate examples

Resources

• Mapbox Expression Reference
• ColorBrewer - Color scales for maps
• Turf.js - Spatial analysis
• Simple Statistics - Data classification
• Data Visualization Tutorials