Palpable Visualizations: Techniques For Creatively Designing Discernible and Accessible Visualizations Grounded In the Physical World

Abstract

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.