What’s My Line? Exploring the Expressive Capacity of Lines in Scientific Visualization
Francesca Samsel - University of Texas at Austin, Austin, United States
Lyn Bartram - Simon Fraser University, Surrey, Canada
Greg Abram - University of Texas at Austin, Austin, United States
Anne Bowen - University of Texas, Texas Advanced Computing Center, Austin, United States
Room: Bayshore III
2024-10-16T14:15:00ZGMT-0600Change your timezone on the schedule page
2024-10-16T14:15:00Z
Abstract
Data is moving beyond the scientific community, flooding communication channels and addressing issues of importance to all aspects of daily life. This highlights the need for rich and expressive data representations to communicate the science on which society rests and on which society must act. However, current visualization techniques often lack the broad visual vocabulary needed to accommodate the explosion in data scale, diversity and audience perspectives. While previous work has mined artistic and design knowledge for color maps and shape affordances (glyphs) in visualization, line encoding has received little attention. In this paper we report on an exploration of visual properties that extend the vocabulary of the line, particularly for categorical encoding. We describe the creation of a corpus of lines motivated by artistic practice, Gestalt theory, and design principles, and present initial results from a study of how different visual properties influence how people associate these into sets of similar lines. While very preliminary, the findings suggest that a rich set of line attributes will support both association and categorical hierarchies, as well as provoke further inquiry into how and why line encoding can be more expressive in encoding multivariate, multidimensional data.