Understanding and Supporting Design Trade-offs for Visualization-based Communication
A shift in the availability of usable tools and public data has prompted mass manufacturing of information visualizations to communicate data insights to broad audiences. Studying professional practice suggests that expert visualization designers and analysts negotiate complex design trade-offs in creating visualizations: decisions between competing design goals that may call for different representations, data selections, or levels of complexity. I will describe several tools and approaches for modeling common trade-offs in order to support more efficient creation and use of data visualizations in online contexts. This includes systems that automate design decisions to produce customized, annotated interactive visualizations to add context to news articles. Successful visualizations also strike a balance between presenting data as subject to uncertainty to support accurate interpretation and depicting data in ways that can be understood by users without statistical background. I will describe a technique for visualizing uncertainty as hypothetical data samples, and present experimental results around the potential benefits of this technique for conveying the reliability of patterns in data. Taken together, these results provide principles and tools that scale effective visual data communication to larger numbers of contexts and users.
Jessica Hullman is a postdoctoral fellow at UC Berkeley in Computer Science. She completed her Ph.D. in information Visualization and HCI at the University of Michigan School of Information between 2009 and 2013, where she worked with advisor Eytan Adar. Hullman also has an M.S.I. in Information Analysis & Retrieval from the University of Michigan School of Information and M.F.A. (Experimental Poetics and Prose) from the Jack Kerouac School for Disembodied Poetics at Naropa University. Hullman is passionate about visual analysis and communication around data. As used online by news organizations, scientists, and data enthusiasts, information visualizations provide context and support deeper analytical insights related to data and text information. Yet supporting the production of high quality visualizations is challenging. Design processes are complex and tacit and professionals hard to find. As abstract representations, visualizations have the potential to mislead or bias interpretations if not designed carefully. Hullman’s work focuses on deepening understanding of trade-offs that affect visualization practice, providing visualization techniques, systems, and knowledge frameworks to support more efficient visualization production and interpretation among diverse audiences. She has studied topics in narrative visualization, or the use of graphics to tell stories around data, and demonstrated approaches for automatically generating and annotating visualizations to accompany news (see forthcoming CHI 2014 paper). She developed a rhetorical framework for understanding how narrative visualizations persuade users to accept a given framing of data. Hullman has studied the potential for viewing order to affect visualization interpretations and proposed a graph-based algorithm for helping designers create effective sets of visualizations for presentation. Hullman believes that better visualization-based communication depends on understanding interpretation among diverse users. She is currently developing a generalizable framework for visualizing uncertainty in ways that even non-statisticians can easily understand. Hullman has also studied factors influencing the interpretation of visualized data for learning, social and crowdsourcing environments.