Three papers were accepted for publication at the Symposium on Visual Information Communication and Interaction (ACM VINCI 2020).

• Carolin Fiedler, Willy Scheibel, Daniel Limberger, Matthias Trapp, and Jürgen Döllner: "Survey on User Studies on the Effectiveness of Treemaps"
• Willy Scheibel, Daniel Limberger, and Jürgen Döllner: "Survey of Treemap Layout Algorithms"
• Daniel Limberger, Matthias Trapp, and Jürgen Döllner: "Depicting Uncertainty in 2.5D Treemaps"

The conference is held as a virtual conference from December 8th until December 10th. More information at vinci-conf.org/.

Treemaps are a commonly used tool for the visual display and communication of tree-structured, multi-variate data. In order to confidently know when and how treemaps can best be applied, the research community uses usability studies and controlled experiments to "understand the potential and limitations of our tools" (Plaisant, 2004). To support the communities' understanding and usage of treemaps, this survey provides a comprehensive review and detailed overview of 69 user studies related to treemaps. However, due to pitfalls and shortcomings in design, conduct, and reporting of the user studies, there is little that can be reliably derived or accepted as a generalized statement. Fundamental open questions include configuration, compatible tasks, use cases, and perceptional characteristics of treemaps. The reliability of findings and statements is discussed and common pitfalls of treemap user studies are identified.

This paper provides an overview of published treemap layout algorithms from 1991 to 2019 that were used for information visualization and computational geometry. First, a terminology is outlined for the precise communication of tree-structured data and layouting processes. Second, an overview and classification of layout algorithms is presented and application areas are discussed. Third, the use-case-specific adaption process is outlined and discussed. This overview targets practitioners and researchers by providing a starting point for own research, visualization design, and applications.

A truthful and unbiased display of data using information visualization requires detecting and communicating uncertainty. Uncertainty is often inherent in data or is introduced by data processing and visualization (e.g., visual display of accumulated data) but frequently not accounted for. This paper discusses the suitability of advanced visual variables such as sketchiness, noise, nesting-level contouring, and color weaving for communicating uncertainty.