Embodied Data Exploration in Immersive Environments: Application in Geophysical Data Analysis

Abstract

Immersive analytics is an emerging field of data exploration and analysis in immersive environments. It is an active research area that explores human-centric approaches to data exploration and analysis based on the spatial arrangement and visualization of data elements in immersive 3D environments. The availability of immersive extended reality systems has increased tremendously recently, but it is still not as widely used as conventional 2D displays. In this dissertation, we described an immersive analysis system for spatiotemporal data and performed several user studies to measure the user performance in the developed system, and laid out design guidelines for an immersive analytics environment. In our first study, we compared the performance of users based on specific visual analytics tasks in an immersive environment and on a conventional 2D display. The approach was realized based on the coordinated multiple-views paradigm. We also designed an embodied interaction for the exploration of spatial time series data. The findings from the first user study showed that the developed system is more efficient in a real immersive environment than using it on a conventional 2D display. One of the important challenges we realized while designing an immersive analytics environment was to find the optimal placement and identification of various visual elements. In our second study, we explored the iterative design of the placement of visual elements and interaction with them based on frames of reference. Our iterative designs explored the impact of the visualization scale for three frames of reference and used the collected user feedback to compare the advantages and limitations of these three frames of reference. In our third study, we described an experiment that quantitatively and qualitatively investigated the use of sonification, i.e., conveying information through nonspeech audio, in an immersive environment that utilized empirical datasets obtained from a multi-dimensional geophysical system. We discovered that using event-based sonification in addition to the visual channel was extremely effective in identifying patterns and relationships in large, complex datasets. Our findings also imply that the inclusion of audio in an immersive analytics system may increase users’ level of confidence when performing analytics tasks like pattern recognition. We outlined the sound design principles for an immersive analytics environment using real-world geospace science datasets and assessed the benefits and drawbacks of using sonification in an immersive analytics setting.