TR2018-063

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 widthinvariant 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.