Data-Driven Guides
Supporting Expressive Design for Information Graphics

Abstract

In recent years, there is a growing need for communicating complex data in an accessible graphical form. Existing visualization creation tools support automatic visual encoding, but lack flexibility for creating custom design; on the other hand, freeform illustration tools require manual visual encoding, making the design process time-consuming and error-prone. In this paper, we present Data-Driven Guides (DDG), a technique for designing expressive information graphics in a graphic design environment. Instead of being confined by predefined templates or marks, designers can generate guides from data and use the guides to draw, place and measure custom shapes. We provide guides to encode data using three fundamental visual encoding channels: length, area, and position. Users can combine more than one guide to construct complex visual structures and map these structures to data. When underlying data is changed, we use a deformation technique to transform custom shapes using the guides as the backbone of the shapes. Our evaluation shows that data-driven guides allow users to create expressive and more accurate custom data-driven graphics.

Authors

Citation

Data-Driven Guides: Supporting Expressive Design for Information Graphics
Nam Wook Kim, Eston Schweickart, Zhicheng Liu, Mira Dontcheva, Wilmot Li, Jovan Popovic, Hanspeter Pfister.
IEEE Transactions on Visualization and Computer Graphics (InfoVis’16), 2017

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Miscellaneous

Nigel Holmes' infographics

In our paper, we used two infographics created by Nigel Holmes, including the monster and factory worker charts (see below). Both infographics were drawn by hand in 1982 which may inevitably contain some errors. We would like to thank Nigel Holmes for allowing us to use his infographics.

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