Localization in Immersive Analytics with World-In-Miniature, Landmarks and Constellations

Immersive analytics is a form of data visualization that allows users to observe and analyze data in multiple dimensions within the virtual environment (VE). Nonetheless, when immersing in such an environment with a large and abstract dataset, users may lose their spatial awareness and be unable to localize themselves within the VE. This issue motivates us to explore visualization techniques to improve the localization ability of the users. Based on the requirements to utilize the dataset’s intrinsic features and explore both ego-and exo-centric reference frames, we propose three techniques to help users localize themselves in VE for immersive analytics: World-in-miniature (WIM), Landmarks and Constellations.