The visual system is the highest-bandwidth pathway into the human brain, and visualization takes advantage of this pathway to allow users to understand datasets they are interested in. Recent scientific advances have led to the collection of larger and more complicated datasets, leading to new challenges in effectively visualizing these data. The focus of this dissertation is on addressing these challenges and enabling the next generation of visualization systems. We address these challenges through two complementary research thrusts: "Advanced Visualization Practice" and "Visualization Design Tools."In our Advanced Visualization Practice thrust, we take steps to extend the process of interactive visualization to work effectively with complicated multivariate motion datasets. We present brushing and filtering operations that allow users to perform complicated filtering operations in a linked-window visualization while maintaining context in complementary views, including two-dimensional plots, three-dimensional plots, and recorded video. We also present the concept of "trends," or patterns of motions that behave similarly over a period of time, and introduce visualization elements to allow users to examine, interact with, and navigate these trends. These contributions help to implement Shneiderman's information seeking mantra (Overview first, zoom and filter, then details-on-demand) in the context of collections of motion datasets.During our work in Advanced Visualization Practice, we realized that there were a lack of tools enabling visualization developers to rapidly and controllably create and evaluate these visualizations. We address this deficiency by our Visualization Design Tools thrust, introducing the idea of visualization creation interfaces where users draw directly on top of data in order to effect their desired changes to the current visualization. In an application of this idea to streamline visualizations, we present a sketch-based streamline visualization creation interface, allowing users to create accurate streamline visualizations by simply drawing the lines they want to appear. An underlying algorithm constrains the input to be accurate while still matching the user's intent. In a second application of this idea, we present a Photoshop-style interface, enabling users to create complicated multivariate visualizations without needing to program. A colormap painting and dabbing algorithm allows users to create complicated colormaps by drawing colors on top of a colormap; an algorithm determines the desired locality of the user's input and updates the colormap accordingly. These interfaces show the potential for future interfaces in this direction to expand the visualization design process to include users currently excluded, such as domain scientists and artists.Through these two complementary thrusts, we help to solve problems preventing newer datasets from being fully exploited. Our contributions in Advanced Visualization Practice solve problems that are impeding the visualization of motion datasets. Our contributions in Visualization Design Tools provide a blueprint for the creation of visualization interfaces that can enable all users instead of just programmers to contribute directly to the visualization design and creation process. Together, these set the stage for future visualization interfaces to better solve our biggest visualization challenges.
University of Minnesota Ph.D. dissertation. November 2014. Major: Computer Science. Advisor: Daniel F Keefe. 1 computer file (PDF); x, 130 pages.
Designing effective motion visualizations: elucidating scientific information by bringing aesthetics and design to bear on science.
Retrieved from the University of Minnesota Digital Conservancy,
Content distributed via the University of Minnesota's Digital Conservancy may be subject to additional license and use restrictions applied by the depositor.