Treemaps are a data-visualization technique for large, hierarchical data sets. They capture two types of information in the data: (1) the value of individual data points; (2) the structure of the hierarchy.

Definition: Treemaps are visualizations for hierarchical data. They are made of a series of nested rectangles of sizes proportional to the corresponding data value. A large rectangle represents a branch of a data tree, and it is subdivided into smaller rectangles that represent the size of each node within that branch.

A hierarchy showing four levels of the S&P 500
A hierarchical tree diagram, showing the structure of the S&P 500. This structure is the basis of the treemap shown below.
Four views of the same S&P 500 treemap, with four levels of the hierarchy of the data set highlighted
FinViz Map of the Market uses a treemap corresponding to the tree structure of the S&P 500 dataset depicted above. The items colored in yellow and marked (A), (B), (C), (D) correspond to the items labeled with those letters in the tree diagram above. (A) represents the entire S&P 500 and is the same as the root of the tree. The rectangle (B) is theTechnology行业。在这内Technologysector, a smaller rectangle isCommunications Equipment(C)。最后,在里面CommunicationsEquipment矩形,所有的小矩形表示dividual companies within that sector, such asCisco系统(D),Qualcomm., and so on. The color of each rectangle shows if the value of that stock is moving up or down – very bright red indicates a big shift downward, and very bright green indicates a big shift upwards.
Two zoomed in images of a treemap, with the relative sizes of items shown, to compare their quantitative size
The size of each rectangle represents the market capitalization (value) of that stock, industry, or sector. At the lowest level of the hierarchy (individual companies), Google (E) has a larger market cap than Facebook (F), and so their relative rectangles are sized appropriately. One level up in the hierarchy (Industry), the entireInternet Information Providers(g)类大于Application Software(H), and their rectangles are appropriately sized to show that differences as well.


Treemaps通常在数据上找到dashboards. Designers often choose them to add visual variety on a dense dashboard. However, treemaps are a complex visualization and present many obstacles to quick comprehension (which is the main requirement for any information displayed on a dashboard).

Treemaps are often used for sales data, as they capture relative sizes of data categories, allowing for quick perception of the items that are large contributors to each category. Color can identify items that are underperforming (or overperforming) compared to their siblings from the same category. This is why FinViz’s Map of the Market is an enduring example of treemaps: it allows users to identify companies that are doing better than their industry peers, even though their overall stock value may be quite small.

Treemaps work well when your hierarchical data has 2 main dimensions that you want to visualize:

  1. Apositive quantitative value,它将表示为矩形的区域(因为区域不能为负,您不能使用Treemaps来可视化增益/丢失等变量,这可以具有正值和负值。)
  2. Acategorical or second quantitative value, which will be expressed as the color of the individual rectangles. If color is used to express a quantitative value, it’s strongly encouraged to use only one color (if all the numbers are positive) or two colors (one for negative and one for positive), and vary the intensity of the color to express precise value. As humans don’t perceive colors to have an inherent order, we strongly recommend that you do not use multiple colors to represent a range of numbers.
An overly colorful treemap
Anexampleof poor use of color in a treemap: several different colors are used to show the percentage of the population that is over 65 in various regions of Europe; each color indicates a different percentage range (with blue marking the low ranges, yellow the middle, and red the high). Unfortunately, there is no universalnatural mappingthat says that blue is less than yellow or than red. Instead, it would have been better if the intensity of single color was used to indicate percentage.

If color represents a categorical variable, it is okay to use different colors for different possible values, as there’s no need for users to interpret a specific color as being “higher” or “lower” than another. However, as with any use of color in a data visualization, restraint in the number of colors is strongly advised!

Regardless of how you use color in a treemap, make the following可访问性accommodations for color-blind users:

  • Avoid using both red and green in the same treemap (especially for values that need to be differentiated quickly).
  • 使用对色盲的人安全的颜色调色板。
  • Test your design with a tool that allows you to simulate a color-blind user’s experience
  • Use a secondary signal (such as text within the rectangle or appearing on hover) for the data aspect captured through color
S&P 500 500 Treemap的版本如色盲模拟器所示
(Left) FinViz’s map of the market uses red and green to encode the change in the stock value (green is up and red is down). (Right) The same visualization is shown as perceived by someone with deuteranopia: the red and green colors are hard to distinguish. In this design, some (but not all) rectangles do contain the recommended alternative cue: for example, the AMZN rectangle in the upper middle is annotated with “+1.28%”, which informs users that this stock is up even if they can’t tell that it’s green. (The second image is from acolor-blindness simulator)。


  • Visually distinct bordersaround higher-level categories help users identify the top-level groupings.
  • High-contrast textensures that people can read the labels inside the treemap rectangles.
  • A visually distinctive selected state,达到用户悬停(或点击)矩形时,帮助用户确认它们正在查看正确的数据点。
  • Additional detail about a selected rectangle(appearing in an overlay), such as the name, value of the variables allows users to drill into the data.

A hover state with a clear border and popup window with additional details
A good example of using a visually distinct border around a selected section of the FinViz treemap, along with additional details about the sector in a popup window.

Treemaps’ Downsides

Comparisons Are Difficult

Human brains are able to process certain visual informationpreattentively: attributes such as length can be grasped quickly and accurately, and values for such attributes can be compared with almost no cognitive effort. Unfortunately, area is not one of these preattentive attributes. Treemaps rely on area (and possibly color) to encode the value of a variable, and therefore, although treemaps can convey overall relationships in a large data set,they are not suited for tasks involving precise comparisons.

A dashboard that includes a treemap.
Thisdashboard包括一个图案,显示了四种工厂生产的各种产品的生产水平。颜色用于指定不同的工厂和大小用于显示生产输出。虽然这种可视化将大量数据压缩到小空间中,但它有效地传达的信息非常高 - 例如,易于识别每个类别中的顶部性能。然而,难以耗时,以确定前五个整体表演者。条形图将比Triemap更准确,更快速地将此信息传达。
A dashboard treemap zoomed in to show the very similar area of two regions.  This is an example of how treemaps are poor tools for precise comparisons, due to the visual similarity.
Zoomed-in view of the dashboard image above: Which is larger, (A) or (B)? Since this visualization uses area to communicate the size of the variable, it does not easily allow for specific comparisons between items. Making this comparison requires hovering over a section, waiting for a tooltip with that value to appear, and then keeping that information in one’s短期记忆while repeating the same process on the other section.

Inefficient for Data that Is Not Hierarchical

Treemaps should not be used if your data is not hierarchical:in those situations, they are functionally equivalent with a pie chart — simply showing a parts-to-whole relationship. (Pie charts are not great visualizations either — like treemaps, they are based on area and angle, attributes that are not preattentive. They should be used only to communicate that one or two items are much larger than the rest, and not for comparing relative sizes of the pie slices.)

Visually Overwhelming

Treemaps are often used to visualize very large data sets, with hundreds or thousands of items. This quantity of information can visually overwhelm users — the treemap becomes a sea of tiny rectangles, many too small to bear a text label. Furthermore, in complex treemaps, the overall hierarchy can easily become undiscernible. The solution is acushion treemap, which uses texture to make each rectangle look “raised” in the center like a cushion and tapering off to the edges. This visual effect takes advantage of humans’ tendency to interpret this type of shading as a raised surface, making it faster to identify the different rectangles.

In a cushion treemap each rectangle has a slight gradient from the edges to the center. This effect helps to visually identify small items as well as larger categories comprised of several rectangles.


Treemaps are also poor choices for data sets with items close in size (i.e.,balanced trees)。在这些情况下,Treemap的主要目的(快速识别给定类别中的最大项目)变得非常困难。最后,用于创建Treemap的标准算法尝试使矩形成为方形的正方形,以便稍微更轻松地进行尺寸比较且易于错误。然而,在随时间改变的交互式可视化中,该算法的伪像是矩形可以随着它们的尺寸改变而移动。结果,随着时间的推移跟踪特定项目变得非常困难。

Alternatives to Treemaps

In many cases, treemaps can be replaced with bar charts (for data that have one quantitative and one categorical variable) or scatter plots (for data with two quantitative variables) that represent the variables of interest.

This process, however, requires an understanding of your users’top tasks; for executives attempting to identify the products that have both a high sales volume and a large profit margin in order to advertise them most aggressively, a 2D scatterplot would be better than a treemap. But if the user cares primarily about the overall sales, a sorted bar chart is a better choice than a treemap. (Sorting is often underappreciated, but is one of the simplest ways of making it easy to identify those items with the biggest and smallest values.)

Scatterplot showing three variables.
A scatterplot is often a better choice than a treemap for visualizing data with 2 quantitative variables, as the human visual system can quickly and accurately detect 2D position. Thisscatterplot还添加了三分之一的分类变量,以颜色表示。


While treemaps can be useful for visualizing certain types of complex, hierarchical data sets, they often hard to interpret. If using a treemap, visually separate the different high-level categories, avoid using multiple colors to express numeric values, and design with color-blind users in mind. Last and foremost, understand what your users need to do with your data and consider whether other visualizations (such as a bar chart or a scatter plot) could replace or augment the treemap.


Ben Shneiderman:“Tree visualization with tree-maps: 2-d space-filling approach,”ACM Transactions on Graphics, 11,1, 92-99. (1992)

Jarke J. van Wijk and Huub van de Wetering:“Cushion Treemaps: Visualization of Hierarchical Information,”IEEE Symposium on Information Visualization (INFOVIS’99), San Francisco, (October 25-26, 1999)