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A tree map chart is like a pie chart. They both partition a shape into
smaller shapes proportional to the data values. A pie chart partition a
circle into slices, while a tree map chart partition a rectangle into
smaller rectangles.
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As a tree map chart partition rectangles into smaller rectangles, it is
possible to recrusively partition the smaller rectangles into even smaller
rectangles. Thus a tree map can have multiple levels of branching, and
that is why it is called a tree map.
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In the previous example, the tree map is colored based on the first level
partition. ChartDirector can also color the rectangles based on the second
level partition or use other custom coloring.
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Squarify is one of the most common methods to partition the rectangle. It
aims to make the rectangles as close to squares as possible. This method
will but the largest rectangle at one corner (configurable, usually the
top-left corner), and the smallest retangle at the opposite corner.
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The Strip method lays out the rectangles from left to right and top to
bottom in the order of the original data, like flowing text (other
directions are also supported). This allows the user to easily look up
a particular rectangle.
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The number of items in each row is such that the rectangles are as close
to squares as possible. As the data are not sorted, a row can contain both
wide and thin rectangles, and the rectangles will not be as close to
squares as the Squarify method.
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Like the Strip method, the Binary Spilt by Size method does not change the
data order.
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This method splits the data series into two shorter series so that their
total data values are as equal as possible. It then splits
the rectangle into two smaller rectangles proportional to the total data
values, and assign the shorter series to the smaller rectangles. The method
is recrusively applied to the shorter series until the each series contains
one item.
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In each step, the rectangle can be splitted left/right or top/bottom. The
direction is chosen such that the smaller rectangles are as close to squares
as possible.
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The Binary Split by Count method is the same as the Binary Split by Size
method, except the data series is splitted in the middle so that the
number of elements in the two series are as equal as possible.