Open Collections

Abstract

There is a fundamental connection between the data we collect and the physical spaces from which it originates. As new technologies have emerged to allow spatial tracking and augmented reality visualization, researchers have increasingly explored ways to define this connection to support intuitive user interfaces and situated data visualization. The central goal of Situated Visualization (SV) is to integrate digital information into its relevant physical context seamlessly. While several frameworks have described aspects of this integration, they remain loosely defined, disconnected, and often fall short of evaluating its quality, specifically, the extent to which a visualization feels like a natural part of the environment rather than a superimposed artifact. To address this gap, this thesis introduces diegesis, a concept from storytelling disciplines that measures an element’s perceived belonging within a world, as a new theoretical lens for SV. This work is the first to formalize diegesis for this domain comprehensively. Through a systematic review of 50 recent works, this thesis introduces the SCORE framework, which quantifies diegesis through five measurable dimensions: Spatial proximity, Concreteness, cOherence, Referential context, and Environmental context. In addition to supporting qualitative comparison of existing designs, the framework provides a quantitative measure of diegesis, enabling SCORE to differentiate visualizations that prior models have treated as equivalent. Building on this, I applied SCORE to a corpus of 67 visualizations, enabling a cluster analysis that revealed five distinct design archetypes and offering a new taxonomy for comparing SV designs and articulating their inherent trade-offs. Finally, a systematic meta-analysis confirms the framework’s practical relevance by demonstrating that higher SCORE values are consistently correlated with favorable usability outcomes. Overall, this thesis provides the SV community with a new theoretical lens, a validated analytical tool, and actionable, evidence-based insights for designing visualizations based on their diegetic profile.