Towards Understanding Time-Varying Spatial 3D Data Analysis with Animation and Small Multiples in Virtual Reality and Desktop

The growing availability of time-varying spatial 3D (S4D) data, such as ocean and atmospheric datasets, has created opportunities for studying dynamic phenomena across time and 3D space. However, designing effective visualizations for S4D data remains challenging due to the high cognitive demands and complexity of these datasets. While techniques like animation and small multiples have been applied in Virtual Reality (VR) and desktop environments, the lack of understanding of analysts’ tasks and challenges limits the development of better visualization techniques. To fill this gap, we conducted an empirical study with domain experts across various fields, comparing four visualization techniques: VR animation, VR small multiples, desktop animation, and desktop small multiples. We identified the strengths and weaknesses of the four techniques, as well as key analytical tasks, current practices, and challenges in S4D data analysis. Finally, we outlined future research opportunities for advancing S4D visualization techniques.