Generative Design Inspiration for Glyphs with Diatoms

Description: 

Sampling from palettes of mark shapes, encoding channels, and scaffold shapes (left), the Diatoms technique generated 20 alternative glyph designs for an urban mobility dataset (center), displayed as small permutables, in which we show a single data point (New York City) drawn according to each of the 20 designs. We highlight two of these designs as small multiples, where every data point is drawn the same way (right).

We introduce Diatoms, a technique that generates design inspiration for glyphs by sampling from palettes of mark shapes, encoding channels, and glyph scaffold shapes. Diatoms allows for a degree of randomness while respecting constraints imposed by columns in a data table: their data types and domains as well as semantic associations between columns as specified by the designer. We pair this generative design process with two forms of interactive design externalization that enable comparison and critique of the design alternatives. First, we incorporate a familiar small multiples configuration in which every data point is drawn according to a single glyph design, coupled with the ability to page between alternative glyph designs. Second, we propose a small permutables design gallery, in which a single data point is drawn according to each alternative glyph design, coupled with the ability to page between data points. We demonstrate an implementation of our technique as an extension to Tableau featuring three example palettes, and to better understand how Diatoms could fit into existing design workflows, we conducted interviews and chauffeured demos with 12 designers. Finally, we reflect on our process and the designers’ reactions, discussing the potential of our technique in the context of visualization authoring systems. Ultimately, our approach to glyph design and comparison can kickstart and inspire visualization design, allowing for the serendipitous discovery of shape and channel combinations that would have otherwise been overlooked.

Watch the supplmental video at https://vimeo.com/576815038. Preprint available on arxiv: https://arxiv.org/abs/2107.09015 (PDF)

Authors: 
Matthew Brehmer
Robert Kosara
Carmen Hull
Publication Date: 
Sunday, October 24, 2021
Publication Information: 
To appear at IEEE VIS 2021 / in IEEE Transactions on Visualization & Computer Graphics