G2

Note: G2 5.0 does not directly support complete map background functionality. This page primarily provides concept introduction. For actual applications, additional map components and data need to be imported.

Introduction to Bubble Maps

A Bubble Map is a visualization chart that uses bubbles (circles) on a geographic map to represent data size at different geographic locations. The size of the bubble typically represents the magnitude of a value, the position represents geographic coordinates, and colors can be used to distinguish different categories or represent another dimension of values.

Bubble Maps are more suitable for comparing geographically related data than Choropleth Maps. The main disadvantage is that when there are too many large bubbles on the map, they may overlap and affect data display, so this needs to be considered when creating the chart.

Other Names: Bubble Chart Map, Geographic Bubble Chart

Components of a Bubble Map

Basic Bubble Map

Chart Type	Bubble Map
Suitable Data	Data containing geographic coordinates (longitude and latitude) and one or more numeric fields
Function	Display geographic distribution and value relationships on a geographic background
Data Mapping	Longitude/latitude fields mapped to map position Numeric field mapped to bubble size Category field can be mapped to color Other values can be mapped to color intensity
Data Volume	Recommended not to exceed 100 data points to avoid bubble overlap affecting readability

The main components of a Bubble Map include:

Geographic Background: Provides a map background with geographic coordinate system, such as world map, country map, etc.
Bubbles: Circular markers where position represents geographic coordinates and size represents the magnitude of values
Color Encoding: Uses different colors to distinguish categories or represent value ranges
Legend: Explains the meaning of bubble size and color

Use Cases of Bubble Maps

Suitable Use Cases

Case 1: Geographic Data Distribution Analysis

Bubble Maps are ideal tools for analyzing data with geographic attributes, clearly showing the distribution pattern of data across geographic space.

Case 2: Regional Comparison Analysis

Through comparison of bubble size and color, different regions' data differences can be effectively compared.

Case 3: Multi-dimensional Geographic Data Display

Bubble Maps can simultaneously display multiple dimensions of information including location, value size, and category.

Unsuitable Use Cases

Case 1: Too Many Data Points Causing Overlap

When there are too many data points on the map, bubbles may overlap and affect clear data display. In such cases, heat maps or dot density maps should be considered.

Case 2: Lack of Geographic Coordinate Information

For data without latitude and longitude information, geocoding conversion is needed first, or other chart types should be considered.

Case 3: Precise Value Display

Bubble Maps focus on showing geographic distribution trends of data and are not suitable for scenarios requiring precise values. Tables or other precise display methods should be used in such cases.

Extensions of Bubble Maps

Multi-layer Bubble Maps

By setting different layers and transparency levels, multiple data series can be displayed on the same map.

Bubble Maps with Map Backgrounds

Combining geographic boundary data provides richer geographic background information.

Time Series Bubble Maps

Showing changes in bubbles over time through animation.

Comparing Bubble Maps to Other Charts

Bubble Maps and Scatter Charts

Bubble Maps use geographic coordinate systems, suitable for displaying geospatial data
Scatter Charts use Cartesian coordinate systems, suitable for displaying correlations between two continuous variables

Bubble Maps and Choropleth Maps

Bubble Maps use bubble size to represent values, allowing precise comparison of values across different regions
Choropleth Maps use color intensity to represent value ranges, suitable for displaying overall distribution patterns of data

Bubble Maps and Dot Charts

Bubble Maps incorporate geographic information, where position has geographic significance
Regular dot charts' positions only represent data dimensions without geographic attributes

Implementing Bubble Maps in G2 5.0

In G2 5.0, we can implement bubble maps by drawing point on geoView. Here are two practical examples:

Example 1: London Population Distribution Bubble Map

Based on London map data, showing the population distribution across different areas:

import { Chart } from '@antv/g2';
import { feature } from 'topojson';

Promise.all([
  fetch('https://assets.antv.antgroup.com/g2/londonBoroughs.json').then((res) =>
    res.json(),
  ),
  fetch('https://assets.antv.antgroup.com/g2/londonCentroids.json').then(
    (res) => res.json(),
  ),
]).then((values) => {
  const [londonBoroughs, londonCentroids] = values;
  const london = feature(
    londonBoroughs,
    londonBoroughs.objects.boroughs,
  ).features;

  // Add simulated population and GDP data to centroid data
  const bubbleData = londonCentroids.map((d, index) => ({
    ...d,
    name: d.name || `Area ${index + 1}`, // Ensure each data point has a name
    population: Math.floor(Math.random() * 500000) + 100000, // 100K-600K population
    gdp: Math.floor(Math.random() * 50000) + 20000, // 20K-70K GDP
    category: ['Business', 'Residential', 'Industrial', 'Mixed'][Math.floor(Math.random() * 4)]
  }));

  const chart = new Chart({
    container: 'container',
    autoFit: true,
  });

  chart.options({
    type: 'geoView',
    children: [
      {
        type: 'geoPath',
        data: london,
        style: {
          fill: 'lightgray',
          stroke: 'white',
          lineWidth: 1
        }
      },
      {
        type: 'point',
        data: bubbleData,
        encode: {
          x: 'cx',
          y: 'cy',
          size: 'population',
          color: 'category',
          shape: 'point'
        },
        style: {
          opacity: 0.7,
          stroke: 'white',
          lineWidth: 1
        },
        scale: {
          size: {
            range: [4, 30]
          },
          color: {
            range: ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728']
          }
        },
        tooltip: {
          title: 'name',
          items: [
            { name: 'Population', channel: 'size', valueFormatter: (value) => `${value ? value.toLocaleString() : 'N/A'} people` },
            { name: 'GDP', field: 'gdp', valueFormatter: (value) => `${value ? value.toLocaleString() : 'N/A'} million` },
            { name: 'Type', field: 'category' }
          ]
        }
      }
    ]
  });

  chart.render();
});

Example 2: Global Major Cities GDP Bubble Map

Displaying GDP distribution of major cities worldwide:

import { Chart } from '@antv/g2';

// Global major cities data - ensuring data completeness
const worldCities = [
  { name: 'Beijing', lng: 116.4074, lat: 39.9042, gdp: 4027, population: 2154, region: 'Asia' },
  { name: 'Shanghai', lng: 121.4737, lat: 31.2304, gdp: 4321, population: 2424, region: 'Asia' },
  { name: 'New York', lng: -74.0059, lat: 40.7128, gdp: 1700, population: 851, region: 'North America' },
  { name: 'Los Angeles', lng: -118.2437, lat: 34.0522, gdp: 710, population: 397, region: 'North America' },
  { name: 'London', lng: -0.1276, lat: 51.5074, gdp: 653, population: 898, region: 'Europe' },
  { name: 'Tokyo', lng: 139.6917, lat: 35.6895, gdp: 1617, population: 1396, region: 'Asia' },
  { name: 'Paris', lng: 2.3522, lat: 48.8566, gdp: 709, population: 1068, region: 'Europe' },
  { name: 'Berlin', lng: 13.4050, lat: 52.5200, gdp: 147, population: 367, region: 'Europe' },
  { name: 'Sydney', lng: 151.2093, lat: -33.8688, gdp: 337, population: 518, region: 'Oceania' },
  { name: 'Toronto', lng: -79.3832, lat: 43.6532, gdp: 324, population: 294, region: 'North America' },
  { name: 'Seoul', lng: 126.9780, lat: 37.5665, gdp: 779, population: 969, region: 'Asia' },
  { name: 'Singapore', lng: 103.8198, lat: 1.3521, gdp: 340, population: 584, region: 'Asia' },
  { name: 'Chicago', lng: -87.6298, lat: 41.8781, gdp: 689, population: 271, region: 'North America' },
  { name: 'Frankfurt', lng: 8.6821, lat: 50.1109, gdp: 731, population: 75, region: 'Europe' },
  { name: 'Hong Kong', lng: 114.1694, lat: 22.3193, gdp: 365, population: 745, region: 'Asia' },
  { name: 'Mumbai', lng: 72.8777, lat: 19.0760, gdp: 310, population: 1284, region: 'Asia' },
  { name: 'São Paulo', lng: -46.6333, lat: -23.5505, gdp: 430, population: 1252, region: 'South America' },
  { name: 'Mexico City', lng: -99.1332, lat: 19.4326, gdp: 411, population: 912, region: 'North America' }
].map(city => ({
  ...city,
  // Data validation and formatting
  name: city.name || 'Unknown City',
  lng: typeof city.lng === 'number' ? city.lng : 0,
  lat: typeof city.lat === 'number' ? city.lat : 0,
  gdp: typeof city.gdp === 'number' ? city.gdp : 0,
  population: typeof city.population === 'number' ? city.population : 0,
  region: city.region || 'Unknown Region'
}));

// Simplified global map outline data
const worldOutline = [
  { type: 'Feature', geometry: { type: 'Polygon', coordinates: [[[-180, -60], [180, -60], [180, 75], [-180, 75], [-180, -60]]] }}
];

const chart = new Chart({
  container: 'container',
  autoFit: true,
});

chart.options({
  type: 'geoView',
  children: [
    {
      type: 'geoPath',
      data: worldOutline,
      style: {
        fill: '#f0f0f0',
        stroke: '#d0d0d0',
        lineWidth: 1
      }
    },
    {
      type: 'point',
      data: worldCities,
      encode: {
        x: 'lng',
        y: 'lat',
        size: 'gdp',
        color: 'region',
        shape: 'point'
      },
      style: {
        opacity: 0.8,
        stroke: 'white',
        lineWidth: 2
      },
      scale: {
        size: {
          range: [8, 40]
        },
        color: {
          range: ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b']
        }
      },
      tooltip: {
        title: 'name',
        items: [
          { name: 'GDP', channel: 'size', valueFormatter: (value) => `${value || 0} billion USD` },
          { name: 'Population', field: 'population', valueFormatter: (value) => `${value || 0} million` },
          { name: 'Region', field: 'region' }
        ]
      }
    }
  ]
});

chart.render();

Key Points

Essential elements for creating bubble maps with G2 5.0:

Use geoView: Create a geographic coordinate system view with type: 'geoView'
Point marks: Use type: 'point' in children to draw bubbles
Encoding mapping:
- Map x, y to longitude and latitude coordinates in the encode object
- Map size to numeric values
- Map color to categories or values
Style settings: Set opacity, stroke, etc. through the style object
Scale configuration: Control bubble size and color ranges through the scale object
Interactive features: Add tooltip and labels to enhance user experience
Multi-layer structure: Use the children array to simultaneously draw map background and bubble layers

G2

Bubble Map