A Human-Centered Approach to Studying the Spatial Visualization of Non-Spatial Information
Today I attended a talk at UT College of Electrical Engineering and Computer Science about the spatial visualization of non-spatial information. Although I would tend to argue that no information lacks a spatial component, it might be better to say "non-geospatial" information. Below is the pertinent information from the talk:
Abstract: Many visual applications, such as visual analytics tools and educational games, employ spatial information presentations to support data exploration and improve understanding. However, it is not well understood how to take advantage of spatial information layouts, especially when dealing with large data sets, abstract information, and multiple display options. As a result, it is often unclear how to effectively design spatial visualizations for learning and sense-making. My research addresses this problem through controlled experimentation and observation. My work focuses on the evaluation of interface design factors for information presentations on physically-large 2D displays and in immersive 3D virtual reality systems. In this talk, I will discuss several projects that evaluate task performance and information processing strategies, with a specific example involving scientific data exploration. Overall, the results suggest that supplemental spatial information can affect mental strategies and support performance improvements for cognitive processing, but the effectiveness of spatial presentations is dependent on the nature of the task and a meaningful use of space. I will close with a discussion of how the lessons learned from user studies affect the design of visual analytics tools.
Bio: Eric D. Ragan is a visual analytics research scientist at Oak Ridge National Laboratory. Eric works within the Situation Awareness and Visual Analytics Team in the Cyberspace Sciences and Information Intelligence Research Group. His research interests include immersive virtual reality, interface evaluation, visual analytics, educational software, training systems, and human-computer interaction. Eric’s research involves human learning and information exploration with spatially distributed visualizations of non-spatial information. He is studying visualization systems that aid organizing evidence, communicate analytic provenance, and support streaming data. Eric received his PhD and MS degrees in computer science from Virginia Tech and a BS in mathematics and computer science at Gannon University. Contact him at raganed>