Stepping into a museum or a cultural exhibition instills a desire within us to explore new topics and cultures. What if we could create a similar spatial structure for visualizing complex and otherwise difficult to approach datasets? In this work, we explore meaningful layouts and organizational schemes for arranging one’s data analysis steps in the virtual environment.
Virtual reality has recently been adopted for use within the domain of visual analytics because it can provide users with an endless workspace within which they can be actively engaged and use their spatial reasoning skills for data analysis. However, virtual worlds need to utilize layouts and organizational schemes that are meaningful to the user and beneficial for data analysis. This paper presents DataHop, a novel visualization system that enables users to lay out their data analysis steps in a virtual environment. With a Filter, a user can specify the modification they wish to perform on one or more input data panels (i.e., containers of points), along with where output data panels should be placed in the virtual environment. Using this simple tool, highly intricate and useful visualizations may be generated and traversed by harnessing a user’s spatial abilities. An exploratory study conducted with six virtual reality users evaluated the usability, affordances, and performance of DataHop for data analysis tasks, and found that spatially mapping one’s workflow can be beneficial when exploring multidimensional datasets
Devamardeep Hayatpur, Haijun Xia, and Daniel Wigdor. 2020. DataHop: Spatial Data Exploration in Virtual Reality. In Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology (UIST '20). Association for Computing Machinery, New York, NY, USA, 818–828. DOI:https://doi.org/10.1145/3379337.3415878