Create your Next Data Visualization in 6 Steps

by Roel Peters and Vinay Gahlot

Treemaps are popular business intelligence dashboards. If constructed well, they outline how the individual parts make up a whole. For instance, they can show how the stocks of an index are made up of the business units of an organization and the channels used in a marketing campaign.

In this guide, you’ll learn about what makes treemap visualizations special and how to create one on Ikigai.

What Is a Treemap Visualization?

A treemap visualization is an enclosure diagram that reveals the topology of a hierarchy to show how the quantities of the individual parts make up the whole. It uses a rectangular (or squarified) layout representing the entire aggregate and is recursively broken down into multiple levels for each categorical part.

Figure 1. Treemap courtesy of Roel Peters

Each level is a representation of another subcategory or dimension. Treemap visualizations can be extended to hierarchical data with any number of levels due to its recursive nature.

Each square represents a category and encodes multiple pieces of information. These categories include:

  • Its surface, which visualizes the size of the measured quantity.
  • Its embeddedness, which characterizes each element’s dimensions and relationship within the hierarchy.
  • Its position, which describes an element’s ranking compared to its sibling elements.
  • And sometimes its color, which represents an additional quantitative or categorical measure (i.e., the rate of change over a period of time).

If the rectangle is large enough, it often contains the name of the category and the additional quantitative measure.

Drawing a Treemap

There are multiple ways to construct a treemap visualization, and you can design slightly different treemaps from the same data set because the rectangle positions and dimensions are defined by the tiling algorithm of your choice. These algorithms optimize space usage and cluster related categories into larger squares.

The following algorithms are three popular methods for constructing a treemap:

1. Slice-and-dice: this classic algorithm moves from the highest to the lowest level through the hierarchy. It partitions each rectangle into the number of subcategories and alternates between horizontal layouts for odd-numbered levels and vertical layouts for even-numbered levels.

2. Squarified: the squarified algorithm’s primary goal is to shape each rectangle as a square and then arrange each element in a decreasing manner from top left to bottom right. To find the best solution, this algorithm is very computing-intensive because there are many possible configurations. Most implementations in today’s business intelligence suites return a good solution that doesn’t require much calculation time.

Figure 2. Squarified algorithm from top left to bottom right

3. Stripped: this algorithm arranges all elements in a decreasing manner from left to right but across multiple horizontal strips.

Many visualization tools support treemaps, including:

Reading a Treemap

To read and interpret a treemap, you need to first find out which quantitative variable is encoded by the surface of each rectangle. Usually, this can be found in the chart’s title, description, or legend.

Then, you can look at the groups or rectangles and establish what dimension or subcategory each level represents.

Next, find out if the algorithm ordered the rectangles and how it did that. Also, find out if they contained a ranking. If the chart is color-coded, you also need to establish its association.

Finally, use specific values and categories of interest to help you make decisions in regard to your organization’s business intelligence needs.

The Benefits of Treemaps

Treemaps can be incredibly helpful because they are versatile and can encode a lot of information. To facilitate the interpretation of complex charts, they often come with a lot of interactivity. For instance, by hovering over or clicking elements, the UI can reveal extra annotations, or the selection of individual elements can enable drill-down navigation in hierarchies with many levels.

Figure 3. Treemap visualization by Ikigai

How to Make a Treemap on Ikigai

Now that the treemap visualization has been introduced, knowing how to generate a treemap can help expand your data visualization toolbox. To make a treemap in Ikigai, the following steps need to be followed:

  1. Upload a dataset on the Ikigai platform. For this example, the sample data will be used (Monthwise). The uploaded data set can be found in the “Datasets” tab.
Figure 4. Monthwise data set.
Figure 5. An uploaded data set in Ikigai.

2. Create a New Chart. Now, click the 3 dots on the right top of the dataset and select “Add Chart”. A pop-up box will open, enter the chart name, select the sheet on which the data is present (if prompted), and click “Create”.

Figure 6. “New Chart” pop-up box in Ikigai.

3. Arrive at the Chart Editor. A chart editor window will open after clicking on the create button. On the right panel of the Chart Editor Window, click on “Table” in the visualization type.

Figure 7. The Chart Editor Window in Ikigai.

4. Select Treemap as Your Visualization Type. Clicking on the table button will open a window for selecting a visualization type. Search for “treemap” and select the type of treemap you want to make and click on “Select”.

Figure 8. Select a visualization type on Ikigai.

5. Select your Treemap Parameters. Now, select the parameter and metrics on which you want to group the tree map and click “Run Query”.

Figure 9. How to specify metrics and groups for a treemap on Ikigai.

6. Your Treemap is Ready! After clicking the run query, the tree map will be ready.

Figure 10. A treemap visualization on Ikigai.

Conclusion

Treemaps are a great way for visualizing how the individual parts of a hierarchically structured data set make up a whole.

In this article, you learned what a treemap is and how to construct a treemap on Ikigai. Because of their complexity, there are a lot of considerations that you should take into account before creating one of your own.

To explore more ways Ikigai can transform your business, read our previous blog posts here or book a demo today.

About the Authors

Roel Peters

Roel Peters is a data generalist with a special interest in making business cases using structured data and simulation techniques.

Vinay Gahlot (Customer Success Manager)

Vinay Gahlot is a Customer Success Manager at Ikigai labs where he works with the clients to make them successfully use the product. He holds an MBA degree from IIM Ahmedabad and has a passion for fitness and sustainability. After work, you will find him playing badminton, doing yoga, or fitness workouts.

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