Visual Computing

University of Konstanz
IEEE Transactions on Visualization and Computer Graphics

Bubble Treemaps for Uncertainty Visualization

J. Görtler, C. Schulz, D. Weiskopf, O. Deussen

Bubble Treemap of the S&P 500 index, decomposed into sectors and companies. Uncertainty arises from aggregating one week of stock data in November 2016. Each circle represents a stock, its area is proportional to the mean closing price, whereas the standard deviation is depicted using the outlines. Our visualization helps to discover a medium-sized sector with low uncertainty and assess its composition (a), as well as a sector with high uncertainty and the company that mostly introduced it (b).


We present a novel type of circular treemap, where we intentionally allocate extra space for additional visual variables. With this extended visual design space, we encode hierarchically structured data along with their uncertainties in a combined diagram. We introduce a hierarchical and force-based circle-packing algorithm to compute Bubble Treemaps, where each node is visualized using nested contour arcs. Bubble Treemaps do not require any color or shading, which offers additional design choices. We explore uncertainty visualization as an application of our treemaps using standard error and Monte Carlo-based statistical models. To this end, we discuss how uncertainty propagates within hierarchies. Furthermore, we show the effectiveness of our visualization using three different examples: the package structure of Flare, the S&P 500 index, and the US consumer expenditure survey.


Hello World!


  author     = {J. Görtler and C. Schulz and D. Weiskopf and O. Deussen},
  journal    = {IEEE Transactions on Visualization and Computer Graphics},
  pages      = {(to appear)},
  title      = {Bubble Treemaps for Uncertainty Visualization},
  year       = {2018},

Supplemental Material

Paper (.pdf, 3.5 MB) Git