A treemap visualizes a part-to-whole relationship. Treemap charts come in handy when you are dealing with large numbers of categories with a hierarchical structure. A treemap consists of multiple categories and each category in the treemap is given a rectangle. The categories could be subdivided into smaller rectangles if you are dealing with subcategories in the data. The size of the area of the rectangles communicates the value. Therefore, treemaps are very useful charts in finding relationships fastly, both within and between categories.
Another benefit of a treemap is the efficient use of space which makes it easy to show a lot of data at the same time. Though, too many rectangles in a treemap can make it difficult to read and can overload the viewer.
That is why you can not use more than three dimensions in our plug-in and we recommend you to be careful with the number of categories used within each dimension.
The treemap is a very helpful chart in showing overall relationships in large datasets but if the data requires more precise comparison, other charts might be a better choice. The human brain is programmed to process the length of an element subconsciously and fast, and the size of an area consciously and slowly. This is why, for precise comparisons, a bar chart might be a better option.
This chart is similar to the simple part-to-whole treemap, but in the nested treemap multiple dimensions are shown. The extra dimension(s) lead to nesting within the first dimension.
This type of treemap uses parallel lines in a vertical or horizontal orientation to divide the rectangles. A combination of both the sliced and diced treemap can also be used where the lines are switched at each level of hierarchy.
In contrast with a standard treemap, in a squarified treemap, all rectangles are as close to a square as possible. This makes it easier to compare them.
Treemaps are perfect to use if you have to visualize a dataset with large quantities of categories. Moreover, if you want to grab the attention, using the treemap might also be a good choice to stand out from other, more popular charts.
The treemap works best with hierarchical data and therefore sorting the rectangles in descending order helps the user to read the chart. This means that the biggest category will be placed on the top left and the smallest category on the bottom right. Reading from left-to-right, top-to-bottom is the natural way (in most languages) to read, which makes it convenient to apply this in a treemap as well.
If you picked the treemap as your chart, you are probably dealing with a lot of data. This also means that you could add potentially a lot of labels to your categories. Since the values in your dataset are represented by the size of the rectangles in the treemap, it could be that some labels do not fit within the rectangle. Make sure to remove those labels that do not fit and keep only the most important labels. Luckily, most of the time the labels that correspond to the largest rectangles of your chart are the most important ones and the ones you want to clarify. If you have important small categories, you can add annotations to the chart to clarify them.
To draw attention to the most important categories of the treemap, a good solution is to highlight these rectangles by adding an outline to the rectangles or filling the rectangle in a specific color and applying a lighter shade of the same color to all the other rectangles. Our brain is programmed to notice deviations instantly. This can be done, for example, by applying changes in size, movement, or color. This way, highlighting a specific rectangle will help catch the reader’s eye immediately.
Coloring of the treemap is a valuable way to communicate your message more clearly and there are multiple ways to do it. Coloring could be used to differentiate between the categories of your chart. Most treemaps have different colors for each category of the first dimension and the subcategories have the same color.
It is also possible to apply performance-based coloring, like change over time or satisfaction. This could be accomplished by using a sequential color palette. You can also show both positive and negative values in your treemap by using a diverging color palette. Though, be careful with showing negative values in a treemap and consider whether using another type of chart isn’t more convenient to use.
Sometimes you are dealing with a lot of categories which can make your treemap very chaotic. If the data allows it, and if it suits the message you want to convey, it is also a possibility to combine all small categories into one “other” category and to label it as such. Ideally, the “other” category is not bigger than the next to the last category.