Voronoi Treemap
A treemap variant that uses organic, curved polygons instead of rectangles — producing a visually striking, cell-like layout for hierarchical data.
// 01 — The chart
What it looks like
Organic, cell-like polygons fill the available space. Each cell's area is proportional to its share — oil dominates while renewables occupy smaller cells.
// 02 — Definition
What is a Voronoi treemap?
A Voronoi treemap is a variation of the traditional rectangular treemap that uses Voronoi tessellation to partition space into irregular, polygon-shaped cells instead of nested rectangles. Each cell’s area is proportional to the data value it represents.
The technique was introduced by Michael Balzer and Oliver Deussen in 2005. It uses an iterative algorithm that starts with random seed points and refines their positions until each cell’s area matches the target value. The result is an organic, almost biological-looking layout.
The main advantage over rectangular treemaps is that Voronoi cells tend to have more uniform aspect ratios — they’re rounder rather than long and thin — which makes it easier to compare cell areas visually. The tradeoff is that the curved boundaries can feel less precise.
Named after: Georgy Voronoi, a Russian mathematician who formalized the concept of partitioning a plane into regions based on distance to a set of seed points — the foundation of this visualization technique.
// 03 — Anatomy
Parts of a Voronoi treemap
// 04 — Usage
When to use it — and when not to
- You want a visually engaging alternative to rectangular treemaps
- Aspect ratio consistency matters — Voronoi cells are more uniform than thin rectangles
- Showing hierarchical data where aesthetics and engagement are priorities
- The data fits within a non-rectangular container (circle, custom shape)
- Creating infographics or editorial visualizations that need visual appeal
- Precise area comparison is critical — irregular shapes are harder to compare than rectangles
- You need to show deep hierarchies (4+ levels) — nesting becomes visually chaotic
- Performance matters — the Voronoi algorithm is computationally expensive for large datasets
- Your audience expects precise, analytical charts — the organic look can seem imprecise
- Labels need to fit neatly — text placement in irregular polygons is challenging
// 05 — Reading guide
How to read a Voronoi treemap
Identify the largest cells
The biggest polygon-shaped cells represent the largest values. Scan for the dominant cells first to understand what categories lead.
Compare cell areas
Larger area = larger value. While irregular shapes make precise comparison harder than rectangles, the relative sizes still convey the hierarchy.
Look for color encoding
Colors typically represent categories, groups, or a secondary data dimension. Check the legend to understand what each color means.
Check for nesting
If the treemap is hierarchical, larger cells may contain smaller sub-cells within them, showing parent-child relationships.
Read the labels
Cell labels identify the data category and often include the value or percentage. Small cells may have truncated or missing labels.
// 06 — Data format
What the data looks like
Like a standard treemap, a Voronoi treemap takes hierarchical data with values at the leaf nodes. The algorithm partitions the container so each cell’s area matches its value.
| Source | Category | Share |
|---|---|---|
| Oil | Fossil | 31% |
| Natural gas | Fossil | 24% |
| Coal | Fossil | 21% |
| Hydroelectric | Renewable | 7% |
| Nuclear | Nuclear | 5% |
| Wind | Renewable | 4% |
| Solar | Renewable | 3% |
// 07 — Construction
How to build one
Place initial seed points randomly or strategically within the container shape.
Compute the Voronoi tessellation — this creates polygonal cells around each seed point.
Iteratively adjust seed positions so each cell's area converges on the target value.
For hierarchical data, recursively subdivide parent cells for child nodes.
Apply colors, labels, and borders. Smooth cell boundaries with curve interpolation if desired.
// 08 — Common mistakes
Mistakes to avoid
Using it for precise analytical work
The organic shapes are beautiful but make exact area comparison difficult. If precision matters, stick with rectangular treemaps or bar charts.
Too many small cells without grouping
Dozens of tiny polygons become illegible. Group small values into an 'Other' category or use a minimum cell size threshold.
Insufficient iterations in the algorithm
If the Voronoi algorithm hasn't converged, cell areas won't match target values accurately. Ensure enough iterations for proper area-weighting.
Inconsistent layouts across updates
Because the algorithm involves randomness, the same data can produce different layouts. Seed the random generator for reproducibility if comparison across versions matters.
// 09 — In the wild
Real-world examples
News organization topic coverage
Newsrooms use Voronoi treemaps to show how much coverage different topics received over a period — the organic shapes feel editorial and magazine-like.
Biodiversity visualizations
Showing species distribution within ecosystems — the organic cell shapes naturally evoke biological imagery.
Market composition
Displaying market capitalization breakdown of sectors in a visually distinctive way for annual reports and investor presentations.
// 10 — Quick reference
Key facts
Also known as
Organic treemap, Voronoi tessellation map
Category
Part-to-whole
Introduced
2005 by Balzer & Deussen
Best for
Visually engaging hierarchical displays
Key advantage
More uniform cell aspect ratios than rectangular treemaps
Complexity
Computationally expensive — requires iterative convergence
// 11 — Accessibility
Making it accessible
Ensure sufficient color contrast between adjacent cells — the irregular shapes make boundaries harder to see.
Add clear text labels with both category names and values inside or near each cell.
Provide a data table alternative for screen reader users.
Use distinct hues rather than relying on opacity differences alone.
Include a legend mapping colors to categories, especially when hierarchy levels use different color schemes.
// 12 — Variations
Chart variations
Circular Voronoi Treemap
Constrained within a circle — creates a visually compact, self-contained visualization.
Weighted Voronoi Diagram
A simpler version without hierarchy — just a flat set of weighted Voronoi cells.
Bubble Treemap
Uses circles instead of polygons — another organic alternative to rectangular treemaps.
// 13 — FAQs
Frequently asked questions
What is a voronoi treemap?+
A Voronoi treemap is a variation of the traditional rectangular treemap that uses Voronoi tessellation to partition space into irregular, polygon-shaped cells instead of nested rectangles. Each cell's area is proportional to the data value it represents.
When should you use a voronoi treemap?+
Use a Voronoi treemap when you want a visually engaging alternative to rectangular treemaps. It also works well when aspect ratio consistency matters — Voronoi cells are more uniform than thin rectangles, and when showing hierarchical data where aesthetics and engagement are priorities.
When should you avoid a voronoi treemap?+
Avoid a Voronoi treemap when precise area comparison is critical — irregular shapes are harder to compare than rectangles. It is also a poor fit when you need to show deep hierarchies (4+ levels) — nesting becomes visually chaotic, or when performance matters — the Voronoi algorithm is computationally expensive for large datasets.
What data do you need to make a voronoi treemap?+
Like a standard treemap, a Voronoi treemap takes hierarchical data with values at the leaf nodes. The algorithm partitions the container so each cell's area matches its value.
How is a voronoi treemap different from a treemap?+
Both a Voronoi treemap and a treemap can look similar at first glance, but they answer different questions. Reach for a Voronoi treemap when the comparisons and patterns it was designed to reveal match what you need to communicate, and choose a treemap when its particular strengths better fit your data and audience.
What is another name for a voronoi treemap?+
Voronoi Treemap is also known as Organic treemap, Voronoi tessellation map. The name varies between fields, but the visualisation technique is the same.
What size of dataset works best for a voronoi treemap?+
Voronoi Treemap works best for Visually engaging hierarchical displays. Outside that range the chart either looks empty or becomes too cluttered to read clearly.
Are voronoi treemaps accessible to screen readers?+
Yes — a Voronoi treemap can be made accessible to screen readers by pairing it with a clear text summary of the key insight, ensuring color choices meet WCAG contrast guidelines, adding descriptive alt text or aria-label to the SVG, and offering the underlying data as an HTML table fallback for assistive technologies.