Introduction

Maps are powerful tools for visual storytelling in journalism. They help readers understand geographic patterns, locate important places, and visualize data across regions. This tutorial will teach you how to create three essential types of maps using Datawrapper: Locator Maps, Symbol Maps, and Choropleth Maps.

Part 1: Locator Maps

What is a Locator Map?

A locator map is the simplest type of map you can create. It's used to highlight specific locations or areas without requiring any dataset. Locator maps are perfect for showing readers where events happened, marking points of interest, or providing geographic context for a story.

Getting Started

  1. Create a New Map: In Datawrapper, click on Locator Map to begin
  2. Find Your Location: You'll see a world map. Use the search function to find your target location (for example, searching for "Chiang Mai, Thailand")
  3. Wait for the Map to Load: The map will automatically zoom in to your selected location

Adding Markers

The map starts with a default circle marker in the center. You can customize this and add additional markers:

  1. Delete the Default Marker: Remove the initial marker if you don't need it
  2. Add New Markers: Click "Add Marker" to place points on your map
  3. Customize Each Marker:
    • Label: Give each marker a descriptive name
    • Icon: Choose from various symbols (numbers, icons, etc.)
    • Position: Drag markers to their exact locations
    • Advanced Options:
      • Change the direction of the label
      • Split labels into multiple lines
      • Adjust colors and scale (make markers bigger or smaller)
      • Add connecting lines between labels and symbols
      • Modify outline colors

Pro Tip: If you know the exact latitude and longitude coordinates, you can enter them directly for precise positioning.

Customizing the Background

Datawrapper offers several background options to enhance your map:

  • Add 3D buildings for urban areas
  • Change base map styles
  • Adjust zoom levels

Adding Context and Publishing

Once your markers are placed:

  1. Add a Title: Create a clear, descriptive title (e.g., "Chiang Mai Famous Locations")
  2. Data Source: Include a link to your data source if applicable
  3. Byline: Add your name for attribution
  4. Alt Text for Accessibility: Write a description for screen readers (e.g., "Simple map showing locations in Chiang Mai, Thailand")
  5. Legend: Add a legend if needed to explain your symbols (optional for simple maps)

Publishing Options

  1. Click Publish and then Publish Now
  2. Choose how to share your map:
    • Copy the Link: Share the direct URL to your map
    • Embed Code: Copy the embed code to place the map on your website
    • Download PNG: Save a static image of your map

You can also enable:

  • Download image option for readers
  • Social media share buttons

Part 2: Symbol Maps

What is a Symbol Map?

A symbol map displays data at specific point locations using symbols (usually circles) that vary in size and color to represent different values. This type of map is ideal for showing data like population, pollution levels, or any metric tied to specific geographic coordinates.

Preparing Your Data

Before creating a symbol map, you need a dataset with:

  • Location names (e.g., city names)
  • Latitude coordinates
  • Longitude coordinates
  • Data values you want to visualize (e.g., pollution levels, population)

For this tutorial, we'll use air pollution data (PM2.5) for Asian cities from stateofglobalair.org.

Data Structure Example:

  • City name
  • Country name
  • Country codes
  • Population
  • Latitude
  • Longitude
  • PM2.5 pollution values

Creating Your Symbol Map

  1. Select Map Type: In Datawrapper, choose Symbol Map
  2. Select Your Base Map: Choose the geographic region (e.g., Asia)
  3. Import Your Data:
    • Select all your data in your spreadsheet (click the top-left cell, hold Shift, then click the bottom-right cell)
    • Copy the data
    • Paste it into the Datawrapper interface
    • Click the import button

Matching Your Data

Datawrapper needs to know which columns contain what information:

  1. Location Name: Select the column with city names
  2. Latitude and Longitude: Datawrapper usually auto-detects these columns
  3. Symbol Size: Choose which variable determines circle size (e.g., population)
  4. Color: Select the variable for color coding (e.g., pollution values)

After matching columns, proceed to check your data. If there are no red warning values, your data is ready.

Refining Your Visualization

In the Visualize stage, you can customize:

Symbol Options:

  • Choose symbol type (circles, arrows, spikes, diamonds, triangles)
  • Adjust maximum symbol size (reduce this if symbols overlap in dense areas)

Color Settings:

  • Select the variable for color mapping
  • Choose a color palette (e.g., red-purple range for pollution data)
  • Switch between continuous colors and steps
  • Use "rounded values" for cleaner legends

Legend Options:

  • Show size legend to indicate what symbol sizes represent
  • Display color scale to show value ranges

Map Interactivity:

  • Enable zoom functionality
  • Crop to data (removes empty areas from view)

Adding Annotations

Make your map more informative with annotations:

  1. Click to add an annotation
  2. Write descriptive text (e.g., "Cities in northern China facing high levels of air pollution")
  3. Drag the annotation to position it
  4. Draw circles or arrows to highlight specific areas
  5. Adjust annotation size and placement

Creating Tooltips

Tooltips appear when users click on symbols:

  1. Add city name
  2. Add your data value with context (e.g., "PM 2.5 average in 2019")
  3. This makes your map interactive and informative

Finishing Touches

  1. Title: Write a clear headline (e.g., "Air Pollution in Asian Cities")
  2. Description: Explain what you're measuring (e.g., "PM 2.5 averages in 2019 for Asian cities")
  3. Data Source: Credit your source (e.g., "State of Global Air")
  4. Click Proceed and then Publish Now

Share your map using the link or embed code.

Part 3: Choropleth Maps

What is a Choropleth Map?

A choropleth map (pronounced "CORE-oh-pleth") colors entire regions—like countries, states, or counties—based on data values. Unlike symbol maps that use points, choropleth maps shade whole areas to show geographic patterns in your data.

When to Use Choropleth Maps

Choropleth maps are ideal for:

  • Comparing data across countries or regions
  • Showing rates, percentages, or averages
  • Visualizing geographic patterns

For this tutorial, we'll recreate a map showing death rates from air pollution across countries using data from Our World in Data.

Preparing Your Data

Your dataset should contain:

  • Region names (e.g., country names)
  • Data values for each region (e.g., death rates per 100,000 people)

Datawrapper will match your region names to geographic boundaries automatically.

Creating Your Choropleth Map

  1. Select Map Type: Choose Choropleth Map in Datawrapper
  2. Choose Your Base Map: Select the geographic area (e.g., World map)
  3. Import Data:
    • Select and copy all your data from your spreadsheet
    • Paste it into Datawrapper
    • Click the import button

Matching Regions

Unlike symbol maps, choropleth maps use region names instead of coordinates:

  1. Region Column: Select the column containing region names (e.g., "Country and Region")
  2. Value Column: Choose which data to visualize (e.g., "2021" values)
  3. Datawrapper will automatically match region names to map areas
  4. Red values indicate regions that couldn't be matched—you can ignore these if they're not essential

Click Proceed to continue.

Styling Your Choropleth Map

Color Settings:

  1. Confirm the correct column is selected for coloring
  2. Choose between continuous colors or steps
  3. Select "rounded values" for cleaner legends
  4. Pick an appropriate color palette (e.g., red shades for negative data like deaths)
  5. Adjust the number of steps (5-7 steps usually work well)

Pattern Options:

  • You can use patterns (like crosshatching) instead of solid colors
  • Most choropleth maps work best with solid colors

Display Options:

  • Show/hide the legend
  • Enable zoom functionality if desired
  • Keep "crop to data" off if showing the full world

Adding Context

  1. Title: Write a headline that highlights your key finding (e.g., "Sub-Saharan Africa and South Asia are Hot Spots for Deaths by Air Pollution")
  2. Description: Explain what the data shows (e.g., "Estimated number of deaths attributed to air pollution per 100,000 people in 2021")
  3. Data Source: Credit your source (e.g., "Our World in Data")

Customizing Tooltips

Edit the tooltip text to make it more descriptive:

  • Instead of just showing the number, add context
  • Example: "Deaths: [value] per 100,000 people"

Publishing

  1. Click Proceed to review
  2. Click Publish Now
  3. Share your map via link or embed code

Best Practices for Map Making

General Tips

  1. Choose the Right Map Type:
    • Locator maps: For showing locations without data
    • Symbol maps: For point data with values
    • Choropleth maps: For regional comparisons
  2. Keep It Simple: Don't overcrowd your map with too many markers or colors
  3. Accessibility Matters: Always add alt text for screen readers
  4. Credit Your Sources: Always include data sources and bylines
  5. Test Interactivity: Make sure tooltips, zoom, and other interactive features work properly

Design Considerations

  • Use color palettes that are colorblind-friendly
  • Ensure sufficient contrast between map elements
  • Keep legends clear and concise
  • Use annotations sparingly to highlight key points
  • Choose symbol sizes that don't overlap excessively

Data Quality

  • Clean your data before importing
  • Ensure region names match Datawrapper's conventions
  • Include latitude and longitude for symbol maps
  • Check for missing or incorrect values

Conclusion

With these three map types—locator maps, symbol maps, and choropleth maps—you have the essential tools for geographic data visualization in journalism. Practice with different datasets and remember that the best maps tell clear, compelling stories that help readers understand geographic patterns and relationships.

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