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One way to visualize hierarchical data is to use tree-maps, a method where rectangles are nested inside larger rectangles. Each piece of data is given a rectangle with an area determined by that data's magnitude in relation to the whole data set. Although it's been used to analyze supply chains, network flow and financial budgets, at Tableau, we believe we have a better method.
Our alternative to tree-maps offers several benefits: ease of comprehension, improved flexibility and ability to provide higher dimensionality. The method simply uses bar charts with size changing in only one dimension. Because the human eye has trouble comparing area – especially when both horizontal and vertical sizes change simultaneously – and can easily compare distance (linear size), a bar chart is perfectly tuned to the human perceptual system. Here is an example of stock data, a typical scenario of treemap proponents, using bars instead of a treemap:
Notice how easy it is, in this example, to see big changes in Stocks with low Capitalization. With treemaps, this is obfuscated because the area of these stocks is very small. Additionally, when analyzing this view in Tableau, the related items highlight for simple identification and comparison.
Furthermore, one of the conclusions in a research paper by Kong, Heer and Agrawala (http://www.scribd.com/doc/49533538/Kong-Heer-Agrawala-2010-Perceptual-Gu...) shows significant benefits to using this approach over Treemaps.
Use Bar Charts at Low Density, Treemaps at High Density
Bar charts resulted in signiﬁcantly lower error when comparing leaf nodes at low densities. If a data set has only a few hundred elements, bar charts are more effective than treemaps.