The mediums, the masses, the methods: towards meeting the demands of immersive analytics

Abstract

Immersive analytics (IA) is a developing sub-domain within the larger fields of information visualization, humancomputer interaction, and extended reality (XR). The focus of IA is to leverage emerging display technologies, such as virtual and augmented reality (VR, AR), to enhance data analysis and information visualization -using their unique affordances for 3D visual encoding, spatial interaction and presentation, and gesture controls. These affordances grant the potential for IA to enhance visualization workflows and tasks beyond what is possible with a 2D display. IA is a young research area, and XR devices, standards, practices, and software are relatively new and rapidly evolving. As a result, IA presents many research opportunities, and IA researchers studying these questions face several challenges. Existing literature has covered several challenges for traditional data visualization on 2D displays. However, data visualizations and their design, methods, and techniques are inherently linked to the mediums used to present them. As a result, traditional methods do not fully translate or encompass the needs of IA and the new medium of XR devices-motivating the need for new domain-specific research.IA demands novel design knowledge for research methods, collaboration modalities, and visualization design. This demand ultimately stems from the XR medium IA seeks to leverage and its relationship with what it can display, how visualizations are designed, and how users utilize it. Meeting the demand for this design knowledge is constrained by the vast and nascent nature of the IA design space, the difficulty of conducting IA design studies, the challenges of remote and collaborative XR user studies, and the complications of disparate display technologies. The work presented in this dissertation seeks to meet these demands and overcome these constraints. I do this through research contributing review analysis of the IA design space, design study methodology, remote user study methodology, and cross-virtually analytics collaboration. These works contribute new domain-specific knowledge, replicated results of real-life studies in VR, qualitative analysis of cross-platform collaborative IA, and a systematic review and analysis of the state-of-the-art in IA research. Through these contributions, this dissertation works towards meeting the demands of IA, allowing future research to be conducted more effectively and efficiently.iii AcknowledgementsIt is difficult to know where to begin acknowledging and thanking the countless people who made this dissertation possible. It's harder still knowing that the following words alone can never convey my gratitude to all. This dissertation is dedicated to all of you. It is a testament to the fact that it takes a village to raise a researcher and that we really stand on the shoulders of giants. I consider myself deeply lucky and privileged to have even had the opportunity to pursue a Ph.D. in computer science in the first place. I am deeply grateful for my early academic advisors, ...