Back to Basics: Sunburst Charts

Sunburst charts, along with their sibling radial visualizations, are powerful tools for representing hierarchical data. Building upon the principles of pie and donut charts, sunburst charts allow multiple series of data to be visualized simultaneously. They help to show quantities at the lowest levels, aggregating these values up through the hierarchy’s branches and leaves. This enables comparison both within categories and across the dataset as a whole.

As usual, here’s a basic sunburst chart you’d typically find in Excel or PowerPoint:

How to Interpret a Sunburst Chart

To get the most from a sunburst chart, follow these steps:

1. Understand the hierarchy: Each level represents a different category or subcategory. The innermost segments represent higher-level categories, while outer segments represent nested subcategories.

2. Analyze segment sizes: The size of each segment indicates its proportional value. Larger segments represent higher values.

3. Identify patterns and trends: Look for patterns, such as clustering or variation, across different levels. Do any categories dominate or stand out?

4. Drill down for details: If interactive, you can drill into segments for deeper insights into subcategories.

5. Consider the context: Always interpret the chart in the context of your dataset and analysis goals, considering any domain-specific knowledge that could affect your insights.

While visually appealing, it’s important to use sunburst charts with care, especially for complex data.

Pros and Cons of Sunburst Charts

Pros:

1. Hierarchical visualization: Sunburst charts excel at showing hierarchical structures, making nested data easy to understand.

2. Proportional representation: The segment sizes visually represent the proportion of each category, facilitating quick comparison of relative values.

3. Interactive exploration: Many sunburst charts support interactivity, allowing users to explore categories in more depth.

Cons:

1. Limited label space: As more segments are added, it becomes harder to display clear labels, leading to potential clutter.

2. Challenging with large datasets: Large or deeply nested datasets can overwhelm viewers and make the chart difficult to interpret.

3. Perceptual distortion: The radial layout can make inner segments appear larger than they actually are, which can lead to misinterpretation.

A Worked Example

For this week’s worked example, I initially considered visualizing the revenue breakdown of large companies. However, I realized this might not be particularly engaging or easy to source meaningful, comparative data for. Reflecting on sunburst charts’ strength in illustrating multiple hierarchies and comparing values, I landed on a more intuitive question: What are the largest football stadiums in England, and how do different tiers of football compare to one another?

In keeping with the goal of making data visualizations accessible to all, here’s an example of a simple, easy-to-understand sunburst chart:

The Wrap-Up

What did you think of the example? Was anything unclear or particularly insightful? Share your thoughts in the comments below—I’d love to hear your feedback!