Space-Partitioning and Containment
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They are used to represent hierarchical structures.
The largest rectangle represents the root node of the tree, and so on.
Tree Maps are the most commonly-used visualization that employ containment. The following shows how to generate a tree map from a simple tree: The areas in the resulting tree map are proportional to the node values.
Area can be used to encode quantity. Color can be used to encode both quantity (color intensity) and category (color hue). Hierarchy is used to encode structure/nesting.
Here, hierarchy represents categories, color represents average profit, size represents the total number of sales.
Tree Maps are extremely scalable!
However, there is a key issue with the slice and dice method: when the rectangles have different aspect ratios (proportion of height vs. width), it becomes difficult to compare areas, especially with elongated rectangles.
To combat this issue, squarified treemaps were introduced. In a squarified treemap, the aim os to have the aspect ratio of each rectangle as close to 1 as possible (thereby making them squares), while still filling the entire chart area. This makes it much easier to compare areas.
Scalability
Node visibility
No overlapping marks, so less clutter
Can encode size and color
Cannot visualize structure directly
Comparisons of rectangles with different aspect ratios is difficult (partially fixed by using squarified treemaps and other alternatives)
Area isn't very effective as a metric for comparison, when compared to other metrics like position
Voronoi Treemaps split the areas in a more organic manner:
Even circular treemaps exist:
