Critical Reflections on Visualization Authoring Systems

Arvind Satyanarayan MIT CSAIL

Bongshin Lee Microsoft Research

Donghao Ren UC Santa Barbara

Jeffrey Heer University of Washington

John Stasko Georgia Institute of Technology

John Thompson Georgia Institute of Technology

Matthew Brehmer Microsoft Research

Zhicheng Liu Adobe Research

IEEE Transactions on Visualization & Computer Graphics (Proc. IEEE InfoVis), 2020

Data binding via dropzones in Lyra (left), via the binding icon in Data Illustrator (middle), and via either approach in Charticulator (right).


An emerging generation of visualization authoring systems support expressive information visualization without textual programming. As they vary in their visualization models, system architectures, and user interfaces, it is challenging to directly compare these systems using traditional evaluative methods. Recognizing the value of contextualizing our decisions in the broader design space, we present critical reflections on three systems we developed-Lyra, Data Illustrator, and Charticulator. This paper surfaces knowledge that would have been daunting within the constituent papers of these three systems. We compare and contrast their (previously unmentioned) limitations and trade-offs between expressivity and learnability. We also reflect on common assumptions that we made during the development of our systems, thereby informing future research directions in visualization authoring systems.