This dissertation investigates techniques to leverage creative processes like sketching, sculpting, and design iteration to improve the discernibility and accessibility of immersive volumetric data visualizations. Discernible visualizations support a viewer's ability to make sense of complexities such as multi-dimensional climate or engineering simulation data. Accessible data visualization both supports the contribution of previously under-utilized design expertise (i.e. artist-accessible visualization design), and subsequently provides access for a broad audience to engage with data through an emphasis on human connection and support for a wide range of displays. Such visualizations aim to provide a palpable, data-driven experience for scientists, artists, and the public. Three early works are presented as a rationale for investigating Palpable Visualizations. Bento Box, an immersive visualization system for comparing multiple time-varying volumetric simulation ensemble instances, demonstrates a current state-of-the-art for scientific data visualization. Weather Report, an interactive site-specific artwork visualizing six decades of weather data, takes an in-depth look at what can be accomplished when designing data-driven experiences in close collaboration with professional designers. And Lift-Off, a VR-based modeling program designed for artists, shows how creative sketching in both the physical and virtual worlds can result in a more accessible environment for both scientific and design-oriented tasks. Based on observations from these three prior works, we present Artifact-Based Rendering (ABR), a framework of algorithms and processes that makes it possible to produce real, data-driven 3D scientific visualizations with a visual language derived entirely from colors, lines, textures, and forms created using traditional physical media or found in nature. ABR addresses three current needs: (i) designing better visualizations by making it accessible for non-programmers to rapidly design and critique many alternative data-to-visual mappings; (ii) expanding the visual vocabulary used in scientific visualizations to enable discernment of increasingly complex multivariate data; (iii) bringing a more engaging, natural, and human-relatable handcrafted aesthetic to data visualization to make the resulting data-driven images more accessible and discernible to the viewer. Finally, we support the accessibility of visualizations through a data streaming and remote rendering pipeline, culminating in demonstrations bridging live supercomputer simulation data with untethered affordable head-mounted AR/VR displays.
University of Minnesota Ph.D. dissertation. June 2020. Major: Computer Science. Advisor: Daniel Keefe. 1 computer file (PDF); iv, 197 pages.
Palpable Visualizations: Techniques For Creatively Designing Discernible and Accessible Visualizations Grounded In the Physical World.
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