Critical Reflections on Visualization Authoring Systems
This work provides insights for researchers and developers in visualization authoring systems, though it is incremental as it builds on existing systems without introducing new methods.
The authors analyzed three visualization authoring systems (Lyra, Data Illustrator, and Charticulator) to compare their limitations and trade-offs between expressivity and learnability, identifying common assumptions to guide future research.
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.