Treemap Charts: A Complete Guide to Using Them

Making smart decisions starts with data. But what happens when your data has layers of complexity? You might have sales figures broken down by region, then by product line, and then by individual products. A simple bar chart just won’t cut it. You need a way to see the big picture and the tiny details all at once. 

This is where the treemap chart shines. Treemaps are a powerful tool for visualizing hierarchical data. They turn complex data sets into a clean, understandable mosaic of rectangles. 

This guide will walk you through everything you need to know about them. We’ll cover what they are, when to use them, how to design effective ones, and even how to build your own. You’ll learn how to transform layered information into a compelling visual story.

What is a treemap chart?

A treemap chart visualizes hierarchical data using a set of nested rectangles. It’s like a diagram of a tree’s branches but packed into a compact, rectangular space. The largest rectangle represents the root of your data tree, and it’s subdivided into smaller rectangles that represent the branches. The area of each smaller rectangle is proportional to a specific value.

This is what makes a treemap unique. Unlike a standard bar chart that uses length to show value, or a pie chart that uses angles, a treemap uses area. This allows it to display two key pieces of information simultaneously: the structure of the hierarchy and the relative size or value of each part. You can instantly see how the whole is divided and how different categories compare to each other.

For example, you could visualize a company’s total revenue (the main rectangle) broken down by department (the first level of smaller rectangles), and then further broken down by individual sales teams within each department (the next level of nested rectangles). The size of each rectangle would show its contribution to the total, giving you a quick, top down view of performance.

When should you use a treemap chart?

Treemaps are incredibly versatile, but they’re not the right fit for every data set. They excel in specific scenarios where you want to understand part-to-whole relationships within a complex hierarchy.

So, when is it a good idea to use one?

• Exploring hierarchical data: Their primary strength is showing nested categories. Examples include visualizing file storage by folders and subfolders, analyzing national economic output by industry and sub industry, or tracking sales data by region, country, and city.
• Comparing proportions: Treemaps make it easy to see the relative size of different components. If you want to know which product categories contribute most to your revenue or which departments are using the most of a budget, a treemap provides an immediate visual answer.
• Identifying major contributors and outliers: Because large values get large rectangles and small values get small ones, you can quickly spot the biggest players in your data set. A massive rectangle immediately draws the eye, highlighting a key area for further investigation.

Advantages of treemaps

Why choose a treemap over other chart types? They offer several key benefits:

• Efficient use of space: They can display thousands of data points within a single, compact view without becoming overwhelming. A bar chart with that much data would require endless scrolling.
• Clear part-to-whole relationships: The nested structure naturally shows how individual elements contribute to the larger categories and the grand total.
• At-a-glance insights: You can quickly grasp the general distribution of your data, identifying large and small categories without having to read individual labels.

When not to use a treemap

Despite their strengths, treemaps have limitations. They’re not ideal when you’re making precise comparisons between categories, especially those with similar sizes. It’s much harder for the human eye to accurately compare areas than it is to compare the lengths of bars.

Avoid using a treemap if:

• Your data isn’t hierarchical. A simple bar or column chart would be better for flat data.
• You need to show precise values or small differences between categories. A table or a bar chart is more effective for this.
• Your data contains a mix of positive and negative values, as treemaps can’t represent negative numbers.

How do treemaps work? The mechanics.

Understanding how a treemap is constructed helps you interpret it correctly and design it effectively. The core concept is simple: A treemap is built on structure, size, and color.

Structure

The structure is the foundation. It starts with a single large rectangle, which represents the total value or the top level parent category. This rectangle is then partitioned into smaller rectangles, each representing a child category. 

The process continues for each level of the hierarchy, creating a pattern of nested rectangles within rectangles. The way these rectangles are arranged is determined by a specific algorithm, often one that aims to create squarish shapes for easier comparison.

Size

The area of each rectangle is directly proportional to a numerical value. If one product category generates twice the revenue of another, its rectangle will be twice the size. This use of area is what allows you to visually weigh the importance of different components at a glance. The bigger the rectangle, the larger its value.

Color

Color adds another layer of information. While size represents one metric (like revenue or population), color can encode a second, independent metric. 

For example, in a sales treemap, you could use size to represent total sales and color to represent profit margin. A large, green rectangle would signify a high-selling, high margin product, while a large, red rectangle might indicate a best seller that isn’t very profitable. 

This dual encoding turns a simple chart into a rich, multidimensional analysis tool.

Data layout

To create a treemap, your data should be properly structured. You typically should have at least two columns: one for the categories (defining the hierarchy) and one for the values (determining the size of the rectangles). For multi level hierarchies, you’ll need separate columns for each level, such as “Region,” “Country,” and “City.”

Types and variants of treemaps

While the basic concept remains the same, treemaps come in a few different flavors. The choice of which variant to use depends on the complexity of your data and what you want to emphasize.

Standard treemap

This is the simplest form, often used for a single level of hierarchy. It divides one large rectangle into several smaller ones, making it great for straightforward part-to-whole comparisons without nested complexity.

Nested or hierarchical treemap

This is the most common and powerful type. It visualizes multiple levels of a hierarchy, with rectangles nested inside other rectangles. This is the go to variant for exploring deep, complex data sets, like an organization chart or a detailed budget breakdown.

Squarified treemap

This variant uses an algorithm to make the rectangles as square as possible. Why does that matter? Because squares and near squares are easier for our eyes to compare than long, skinny rectangles. 

A squarified layout improves readability and makes it simpler to gauge the relative sizes of different segments, especially when they’re close in value. Most modern data visualization tools use a squarified algorithm by default.

Slice-and-dice treemap

This older algorithm works by dividing the space either horizontally or vertically at each level of the hierarchy. It often results in very elongated rectangles that are difficult to compare and label. While it’s simple to compute, the squarified approach is generally preferred for its superior visual clarity.

Design best practices and common pitfalls

A poorly designed treemap can be more confusing than helpful. Following a few design principles can make the difference between a cluttered mess and a clear insight.

Use a smart color scheme

Color should be used purposefully. For categorical data, use a distinct color for each main category. For continuous data, use a sequential or diverging color palette. 

A sequential palette (e.g., light blue to dark blue) is great for showing magnitude, like low to high sales. A diverging palette (e.g., red to white to green) is perfect for showing a range with a central point, like profit margins from negative to positive. 

Avoid using too many colors, as it can create distracting visual noise.

Limit the hierarchy levels

While treemaps can handle deep hierarchies, displaying too many levels at once can make the chart unreadable. 

Aim to show no more than two or three levels at a time. For deeper analysis, consider using interactive features that let people drill down into specific categories.

Keep labels readable

Labels are crucial for understanding. Make sure the text is readable on both large and small rectangles. If a rectangle is too small to fit a label, rely on tooltips that appear when someone hovers over it. 

Place labels for parent categories in a prominent position, like the top of the rectangle, to clearly define the groups.

Avoid excessive detail

A treemap with hundreds of tiny, unlabeled rectangles is often called a “Ben Shneiderman treemap,” after its inventor, who used it to visualize massive file directories. 

While useful for spotting outliers in that context, for general business reporting it just creates clutter. If you have many small categories, consider grouping them into a single “Other” category.

Use borders and whitespace

Subtle borders or small gaps between rectangles can help distinguish between categories and clarify the hierarchical structure. Use them to define parent groups without overwhelming the visual.

Sort for clarity

Arrange the rectangles in a logical order, such as from largest to smallest. This creates a predictable pattern and makes it easier for viewers to find what they’re looking for.

Telling a story with a treemap

A treemap is more than just a chart. It’s a storytelling tool. By guiding your audience through the visualization, you can reveal insights and highlight important patterns.

Start by introducing the whole. Explain what the entire rectangle represents, such as “Total Company Revenue for the Year.” Next, move to the main categories. Point out the largest rectangles and explain what they are. For example, “As you can see, the Electronics and Apparel departments account for nearly 70 percent of our total revenue.”

Then, dive into the details. Use color to add another layer to your story. “Within the Electronics department, notice this large, bright green rectangle. That is our new line of smartphones, which has not only high sales but also our best profit margin.” 

Finally, point out any anomalies or surprises. A small rectangle with a bright, alarming color might indicate a problem requiring attention. By narrating the chart this way, you turn abstract data into a more concrete story that people can act on.

How to create a treemap chart

Creating a treemap has become straightforward with modern tools. Whether you’re using a spreadsheet program or a dedicated business intelligence platform, the general steps are similar.

Step 1: Prepare your data

First, structure your data correctly. You’ll need one or more columns to define the hierarchy and one column for the numerical values that will determine the size of the rectangles.

For a simple treemap, your data might look like this:

For a nested treemap, you’ll need more category columns:

Step 2: Choose your tool and insert the chart

• In Excel: Select your data range, go to the “Insert” tab, click on “Insert Hierarchy Chart,” and choose “Treemap.” Excel will automatically generate the chart for you.
• In BI Tools (like Power BI, Tableau, or Domo): These tools offer more advanced treemap capabilities. You’ll typically drag your category fields into a “Category” or “Group” well and your numerical field into a “Values” or “Size” well. You can also drag a second numerical field to the “Color” well to add another dimension.

Step 3: Customize and refine

Once the chart is created, you can customize it. Adjust the colors to match your story, format the labels for clarity, and refine the tooltips to provide extra context. In BI tools, you can add interactive filters or drill down actions, allowing people to explore the data on their own.

Limitations and alternatives

Treemaps are powerful, but they’re not always the best choice. It’s important to know their weaknesses and when to use an alternative visualization.

Limitations

• Poor for precise comparisons: As mentioned, comparing the area of two similarly sized rectangles is difficult. If you’re looking for exact comparisons, a bar chart is a better option.
• Small categories get lost: Very small values result in tiny rectangles that are hard to see, click, or label. This can cause important but small data points to be overlooked.
• No negative values: Treemaps are based on area, which can’t be negative. If your data set includes negative numbers, you’ll need to use a different chart type, like a waterfall or bar chart.

Alternatives to treemaps

• Bar charts (grouped or stacked): When you want precise comparisons or your data isn’t deeply hierarchical, a bar chart is often clearer.
• Sunburst chart: Like a treemap, a sunburst chart shows hierarchical data. However, it uses a circular layout of rings, which some people find more intuitive for showing levels but can be harder to read for comparing proportions.
• Sankey diagram: If you want to show flow or movement between categories in a hierarchy, a Sankey diagram is an excellent alternative.
• Simple table: Sometimes, the best visual is no visual at all. If you need to show precise values for many different items, a well formatted table might be the most effective way to communicate your data.

Conclusion and key takeaways

Treemap charts are an excellent way to visualize hierarchical data in a compact and intuitive format. They allow you to see both the overall structure and the relative contributions of its parts in a single glance. By using size and color effectively, you can pack a tremendous amount of information into one visualization and tell a powerful data story.

Remember to match the chart to your data and your goal. Keep the design clean, prioritize readability, and always focus on clarity over complexity. When used correctly, a treemap can reveal insights hidden in your data so you can communicate complex information with ease.