Data-Driven Guides

with Nam Wook Kim, Eston Schweickart, Mira Dontcheva, Wilmot Li, Jovan Popovic, Hanspeter Pfister

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.

We present Data-Driven Guides (DDG), a technique for designing expressive information graphics. 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.


Data-Driven Guides: Supporting Expressive Design for Information Graphics
PDF (InfoVis 2016)

Supplemental Materials

Deformation algorithm for vector graphics


Try the tool (alpha version)

More Information

Project page on Nam's website

Related Project

Data Illustrator