Two dimensions for organizing immersive analytics: toward a taxonomy for facet and position

A table provides examples of the content of the taxonomy: the rows indicate the size of the visualization (from small to large) and the columns indicate the position of the visualization (either Free Global, Fixed Global or Situated).
We conceptualize a taxonomy that organizes immersive analytics according to two dimensions: spatial and visual presentation. Each intersection of this taxonomy represents a unique design paradigm which, when thoroughly explored, can aid in the design and research of new immersive analytic applications.
Abstract
Research involving Virtual Reality (VR) headsets is becoming more and more popular. However, scaling VR experiments is challenging as researchers are often limited to using one or a small number of headsets for in-lab studies. One general way to scale experiments is through crowdsourcing so as to have access to a large pool of diverse participants with relatively little expense of time and money. Unfortunately, there is no easy way to crowdsource VR experiments. We demonstrate that it is possible to implement and run crowdsourced VR experiments using a pre-existing massively multiplayer online VR social platform—VRChat. Our small (n=10) demonstration experiment required participants to navigate a maze in VR. Participants searched for two targets then returned to the exit while we captured completion time and position over time. While there are some limitations with using VRChat, overall we have demonstrated a promising approach for running crowdsourced VR experiments.
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Two dimensions for organizing immersive analytics: toward a taxonomy for facet and position

David Saffo, Sara Di Bartolomeo, Caglar Yildirim, and Cody Dunne. Proc. 4th Workshop on Immersive Analytics: Envisioning Future Productivity for Immersive Analytics—IA Workshop. 2020. DOI: 10.31219/osf.io/pk2rq

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