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: