This article is a group effort, with contributions from the authors of our InfoVis’19 paper.

TLDR: A new generation of visualization authoring systems has emerged in the past few years. Designed to support a common goal, these systems vary in terms of their visualization models, system architectures, and user interfaces. What are the strengths and weaknesses of these systems? How do we choose the right tools to build our visualization? We propose to use critical reflections as a method to compare these systems.

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Chris is a visualization enthusiast, with a background in graphic design and some basic knowledge of data processing in Excel. An avid follower of numerous visualization design blogs and podcasts, Chris likes to apply the design knowledge and practices learned from these sources when making data visualizations. Template based tools like Excel charts are not flexible enough for novel visualization designs. Programming toolkits like D3 are powerful, but they also seem quite daunting to someone like Chris who has never coded before. Drawing tools like Adobe Illustrator are great for sketching, but lack functionality for encoding data as visual properties of graphical elements. …


Zhicheng "Leo" Liu

Research scientist at Adobe, interested in information visualization, visual analytics and cognitive science.

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