What Is a Treemap Chart? (Common Data Visualization Type #7)

In the ever-evolving world of data analytics, data visualization stands as a cornerstone for interpreting and communicating complex information. One particularly powerful chart type for representing hierarchical data is the treemap chart.

In this article, you'll learn:

• What a treemap is and how it works
• When to use a treemap (and when you shouldn’t)
• Real-world application scenarios across industries
• The key benefits and limitations of treemaps
• Popular tools you can use to build treemap visualizations
• Practical design best practices for clear, readable treemaps

What Is a Treemap?

A treemap is a chart that uses nested rectangles to represent data. Each rectangle’s area is proportional to the corresponding data value, making treemaps especially effective for displaying large sets of hierarchical or part-to-whole data.

You can think of it as a mosaic: each “tile” represents a node in your hierarchy, and its size shows how important it is compared to the whole.

Origins and Evolution

Treemaps were invented in the early 1990s by Ben Shneiderman at the University of Maryland to visualize the contents of a hard drive. His approach partitioned the available space into progressively smaller rectangles so every part of the hierarchy could be shown at once.

Since then, treemaps have evolved from static, research-oriented charts into interactive dashboard components. Modern tools allow you to:

• Hover for tooltips
• Click to zoom into subcategories
• Filter and highlight specific paths in the hierarchy

This makes treemaps a popular choice in BI dashboards, analytics tools, and monitoring systems.

When Should You Use a Treemap?

Treemap charts are a great fit when you need to:

• Show hierarchical data
  • Example: Category → subcategory → product
• Compare parts to a whole with many categories
  • Example: revenue contribution of hundreds of SKUs
• Pack a lot of information into a compact area
  • Example: limited dashboard real estate
• Highlight the largest or smallest contributors at a glance
  • Example: which region or product line is dominating performance

Typical data patterns that work well:

• Sales by category → subcategory
• Budget allocations by department → team
• Web traffic by channel → campaign
• Error counts by service → endpoint

If your main questions are “What takes up the most space?” or “Which categories dominate the whole?”, treemaps are a strong candidate.

When You Shouldn’t Use a Treemap

Treemaps are not always the best choice. Avoid them when:

• You need precise value comparisons
  • Use a bar or column chart if users must compare exact numbers accurately.
• The hierarchy is shallow and labels matter more than area
  • A simple bar chart or table might be clearer.
• Your users are unfamiliar with treemaps and have limited time or training
  • Simpler charts may be more accessible.
• You have only a few categories (e.g., 3–5)
  • A bar or pie chart will be more straightforward.

As a rule of thumb:

Use treemaps when density and hierarchy are key.
Use bars/columns when accuracy and ranking are key.

Treemap vs Other Chart Types

Here’s a quick conceptual comparison:

• Treemap vs Bar Chart

  • Treemap: Great for dense, hierarchical part-to-whole views.
  • Bar chart: Better for precise comparisons and ordering categories.

• Treemap vs Pie Chart

  • Treemap: Handles many categories and subcategories.
  • Pie chart: Works only for a small number of categories (e.g., 3–7).

• Treemap vs Sunburst Chart

  • Treemap: Uses area (rectangles) to show hierarchy and magnitude.
  • Sunburst: Uses radial segments; sometimes more intuitive for sequential hierarchies but less space-efficient.

A Simple Treemap Example

Imagine an e-commerce store that wants to analyze monthly sales across product categories and subcategories.

Sample Data

In a treemap:

• Each category (Electronics, Home, Fashion) is a large rectangle.
• Each subcategory is a rectangle nested inside its parent category.
• The area of each rectangle is proportional to its sales.

From a glance, you can see:

• Electronics dominates overall sales.
• Within Electronics, Phones is the top subcategory.
• Fashion and Home still contribute significantly but less than Electronics.

If your tool supports interactivity, hovering over each rectangle might show a tooltip like:

Category: Electronics
Subcategory: Phones
Sales: $180,000
Share of Total: 27%

Applications of Treemaps

Treemaps are used across many industries wherever hierarchical or segmented data needs to be summarized visually.

Corporate Dashboards

Treemaps shine on executive dashboards, particularly for financial and portfolio data:

• Visualize portfolio allocation by asset class, sector, region, or risk level.
• Quickly spot overexposed positions or underperforming segments.
• Drill down from “All assets” to specific holdings.

This makes pinpointing high-performing areas or areas of concern far easier than scanning long tables.

Marketing Analytics

Marketing teams are flooded with data—from channels to campaigns to audiences. Treemaps can help:

• Show which channels drive the most traffic or revenue.
• Break down performance by campaign, audience segment, or geo region.
• Quickly identify segments that either overperform or underperform expectations.

For instance, if you’re launching a new product, a treemap can reveal which age group or region contributes most to sign-ups or revenue.

Health Sector

The healthcare industry provides rich hierarchical data:

• Disease types → subtypes
• Hospitals → departments
• Regions → districts

Treemaps can be used to visualize:

• Incidence rates by disease and region
• Hospital occupancy by department
• Resource allocation across facilities

During the COVID-19 pandemic, treemaps were often used to highlight infection or hospitalization rates across regions, helping decision-makers prioritize resources.

E-commerce

E-commerce platforms can harness treemaps to monitor:

• Sales by category, brand, and product
• Inventory levels by warehouse and SKU
• Customer behavior by segment and region

By scanning a treemap, an e-commerce manager can quickly see:

• Which products are top sellers
• Which categories are underperforming
• Where inventory is heavily concentrated

This supports faster, more informed decisions around pricing, promotions, and stock.

Benefits of Using Treemaps

1. Space Efficiency

One of the standout advantages of treemaps is their ability to make efficient use of screen space. Unlike pie or bar charts, they fill the entire visualization area, allowing you to display many categories and subcategories in a compact layout.

2. Hierarchical Clarity

Treemaps perform well in representing hierarchical data:

• You can view both the big picture (top-level categories) and the details (subcategories) in a single chart.
• Zooming and filtering allow users to move between overview and detail seamlessly.

This “zoom in, zoom out” capability makes treemaps a strong fit for exploratory analysis and drill-down dashboards.

3. Visual Impact

A well-designed treemap does more than present numbers—it tells a story:

• Larger rectangles immediately draw attention to the biggest contributors.
• Color gradients can encode additional metrics (e.g., growth rate, margin, or risk level).

This makes it easy for stakeholders to spot outliers, clusters, and patterns without reading every label.

Limitations of Treemaps

Overcrowding

One downside is that treemaps can become cluttered with too many categories:

• Very small rectangles are hard or impossible to read.
• Long labels may be truncated or overlap.

If you have hundreds or thousands of categories, consider:

• Aggregating small values into an “Other” group
• Showing only the top N categories and filtering the rest
• Using interactive drill-down instead of showing all levels at once

Limited Precision

Treemaps are excellent for relative comparison, but not for precise numeric comparison:

• Users can easily see which category is bigger.
• It’s harder to see whether one rectangle is, say, 5% larger than another.

If precise comparison is important, pair the treemap with:

• A bar chart
• A table showing exact values
• A tooltip with detailed metrics

Primarily Quantitative

Treemaps are well-suited for quantitative metrics (e.g., revenue, counts, area), but they do not directly explain why those values occur:

• They show “what” and “how much,” not “why.”
• Qualitative analysis often needs additional charts, text, or commentary.

Tools for Creating Treemaps

Modern tools make it straightforward to build interactive treemap visualizations. Here are some popular options:

Tableau

Tableau is a leading BI and data visualization platform:

• Drag-and-drop interface to build treemaps in seconds.
• Rich interactivity (tooltips, filters, highlighting).
• Easy integration with a wide range of data sources.

Microsoft Power BI

Microsoft Power BI offers built-in treemap visuals:

• Customize colors, labels, and tooltips.
• Combine treemaps with slicers and other visuals on a single dashboard.
• Integrates seamlessly with Excel, SQL Server, and other Microsoft tools.

Looker Studio (formerly Google Data Studio)

For those who prefer cloud-based, Google-native solutions, Looker Studio (formerly Google Data Studio) is a strong option:

• Free to start, with connectors to Google Analytics, BigQuery, Sheets, and more.
• Supports treemap-like visuals via community or built-in charts depending on configuration.
• Good for lightweight dashboards and quick reporting.

D3.js

For developers who want full control, D3.js is a powerful JavaScript library:

• Unparalleled customization for interactive treemap layouts.
• Fine-grained control over animation, color, and behavior.
• Requires coding skills, but rewards you with maximum flexibility.

Excel

Excel might sound basic, but its treemap capabilities shouldn’t be underestimated:

• Built-in Treemap chart type in the “Insert Chart” menu.
• Effective for quick exploration and smaller datasets.
• Ideal for users who already work heavily in spreadsheets.

R Programming

For data scientists, R provides powerful packages such as:

• treemap
• treemapify
• d3treeR

These packages make it easy to create highly customized treemaps directly from your R workflows.

Designing an Effective Treemap: Best Practices

Keep It Simple

A treemap is already visually dense. To keep it readable:

• Limit the number of color encodings (ideally just one main metric).
• Avoid unnecessary borders, patterns, or decorative elements.
• Ensure text labels are legible—prioritize the most important levels.

Manage Hierarchical Levels

As a guideline:

• Try not to go beyond two or three levels of hierarchy in a single view.
• If deeper levels are required, use:
  • Drill-down interactions
  • Separate views or tabs
  • Filters to show only one branch at a time

Too many levels at once can overwhelm viewers and dilute insight.

Use Interaction Wisely

Interactivity turns treemaps from static pictures into exploratory tools:

• Hover for tooltips that show detailed metrics.
• Click to zoom into a category or filter the rest of the dashboard.
• Highlighting to show related rectangles when a legend or filter is selected.

But be careful: too many interactive behaviors at once can confuse users. Focus on the interactions that directly support the analytical questions at hand.

Thoughtful Color Coding

Color should clarify, not distract:

• Use color to encode one additional dimension (e.g., growth rate, profitability, or status).
• Prefer color scales that match the meaning:
  • Diverging scales (red–white–green) for positive vs negative changes.
  • Sequential scales for magnitude (light to dark).
• Avoid more than a handful of distinct categorical colors at once.

Use Real-World Context

Ground your treemap in real-world context:

• Mention the time period, currency, or units in titles or tooltips.
• Annotate key findings:
  • “Category A drives 40% of total revenue.”
  • “This branch shows negative growth despite high revenue.”

This helps stakeholders connect the visualization to decisions they need to make.

Conclusion

Treemaps are a versatile, visually rich, and space-efficient way to represent hierarchical and part-to-whole data. They excel when you need to:

• Summarize complex hierarchies in a compact view
• Reveal which categories contribute most to the whole
• Let users explore data through interactive drill-down and filtering

Whether you’re a financial analyst, marketer, healthcare professional, or e-commerce manager, treemaps can uncover patterns that might be hidden in tables and traditional charts.

With tools like Tableau, Power BI, Looker Studio, D3.js, Excel, and R, creating treemaps is more accessible than ever. Just remember to:

• Use treemaps when density and hierarchy matter
• Pair them with simpler charts when precision is required
• Keep the design clean, hierarchies manageable, and colors purposeful

Happy visualizing!