Enhancing Disease Maps with Labels in R

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The GRAPH Course
Data Reporting
Epidemiology
public health
Data Visualization
R
Author

The GRAPH Network

Published

February 18, 2025

Maps are powerful storytelling tools in geospatial data visualization. However, a map without clear annotations and labels is like a book without titles or chapter headings—while the story exists, it becomes much harder to understand, interpret, and appreciate.

Through this lesson, I gained a deeper understanding of how proper annotation transforms a simple visualization into an informative guide. Precise labeling helps highlight areas of interest, making it easier for viewers to grasp complex spatial data and its narrative.

Key Skills Acquired

After completing this course, I was able to:

  • Enhance Choropleth Maps – Integrate continuous data indicators for better granularity.

  • Overlay State Names Clearly – Ensure readability without cluttering the visualization.

  • Combine Labels with Data Trends – Seamlessly integrate state names with increasing rates while maintaining legibility.

  • Highlight Key Regions – Apply techniques to emphasize specific areas while preserving overall map context.

  • Optimize Point Placement – Strategically position labels to improve clarity in geospatial visualizations.

By mastering these techniques, I can now create detailed, insightful, and user-friendly maps that effectively communicate spatial data.

For a step-by-step guide and examples, visit this site.