Equal-height treemaps for multivariate data

A well-known limitation of classic continuous treemaps is that they generally provide two (or at most a few) visual mappings for data variables apart from the hierarchical relationships. Typically, one variable maps to cell area; another maps to color. However, many data-centric tasks require human users to consider multiple variables simultaneously. The current work introduces the concept of equal-height, variable-width cells in treemaps, which affords the packing of multiple variables into the cell areas of the terminals of the hierarchy. We demonstrate how color and some largely width-invariant graphs can be utilized in the cell areas to add additional visual information in a multi-variate treemap. Examples come from machine learning and from finance applications.