Treemap data visualizations
make it easy to spot anomalies & outliers

Treemaps and its cousins — Heatmap and Heat Matrix visualizations — are three of most useful data visualization techniques available. They provide an efficient window into underlying real-time business data. You can spot anomalies, hidden patterns and outliers in seconds when looking at our Treemap.

When looking at a Treemap, you will be able to point out important values in a matter of seconds. However, what most people are unaware of is that your brain processes the information before your mind becomes aware of it. For the brain, it is not a question of seconds, but milliseconds. Scientists call this pre-attentive processing. It is defined as involuntary processing of visual information without focused attention in a very short time - as fast as 200 milliseconds.

This automated and effortless perception of information is what visualization tools such as treemaps make use of. A picture really does say more than a thousand words.

Creating a Treemap Step-By-Step

With Panopticon's powerful applications, a data visualization based on a Treemap is created within seconds. We start from an ordinary data table:

The four columns in the table contain the name of the company, the industry sector it belongs to, the market capitalization value of the company and the stock price change during the last day. Imagine that this is a list of the stocks in a portfolio. There is data, and there is some structure.

Let's sort the list alphabetically with respect to the sector column, thereby grouping the two different sectors into groups which can be easily distinguished: Food Products and Soft Drinks. Regardless of what sector each company belongs to, they all belong to the Stocks dataset. Within all stocks, there are two groups in the sector category, each with stocks in it. We can arrange and present the data like a tree.

The previous picture was a traditional view of a tree structure, where parent child relationships are depicted as links between separate branches in the tree. This hierarchy can also be represented differently, by using parent child relationships. By drawing boxes in boxes, the rectangles become nested components.

Stocks, the group representing the totality of the entire dataset, is the large box framing everything else. Within this group, there are two smaller boxes representing each sector, which in turn contain its individual companies.

At this point, the picture shows all rectangles having equal sizes. The next step is to let the sizes and colors of rectangles be determined by the data.

Instead of letting all rectangles have the same color and an equal size, we base colors and sizes on column values from the table. The color dimension can be used to denote the one-day-change in the stock value - we let deep red signify a strong value decrease, and deep blue a strong value increase. White would represent neutral, zero change. We also let the size of rectangles represent the market cap column in the table. Coca-cola is the largest one and all comparative relations with all other stocks including the sectors are immediately obvious from the image.

Take our interactive visualization quiz and see how fast you can spot important business information with our Treemap visualization.

Treemap Availability

Treemaps data visualizations are included in all of our products, including our Windows desktop product, our web-deployed Enterprise solution and our SDK.