Cover Sheet Title: CAREER: Effective Interaction Design in Data Visualization PI: Arvind Satyanarayan, NBX Career Development Assistant Professor, MIT E-Mail:
[email protected] CAREER: Effective Interaction Design for Data Visualization Society’s broad adoption of data visualization has been driven, in part, by decades of research developing theories of effective visual encoding, and instantiating them in software systems to lower the threshold for authoring visualizations. However, there has been little analogous theory-building for interactivity — a feature widely thought to be critical for effective visualization as it enables a tighter feedback loop between generating and answering hypotheses. Comparative empirical studies of interactivity are rare, and have been conducted in an ad hoc fashion; and, interaction taxonomies do not provide design guidelines such as how to pick between different techniques for a given task. For instance, does the data distribution affect whether zooming should occur via buttons, continuous scrolling, or brush-to-zoom? Similarly, how should interactive filters be depicted (e.g., highlighting selected points, dimming unselected points, or removing them from the chart altogether) to more easily perceive trends in the data? And, critically, how do these interaction design choices affect dataset coverage, the rate of insights, and people’s confidence? This lack of theory has also impeded support for interaction design in visualization systems. While recent work has explored higher-level abstractions for authoring interactivity, users must still manually invoke and wire the necessary components together, with little of the guidance and support that accompanies the visual encoding process. Moreover, with no established conventions for interaction design, authors and consumers must contend with inconsistent and unreliable experiences — for instance, by default, dragging may pan the chart, highlight brushed points, or zoom into the selected region depending on the tool used.