Time-varying geospatial data presents some specific challenges for visualization. Here, we report the results of three experiments aiming at evaluating the relative efficiency of three existing visualization techniques for a class of such data. The class chosen was that of object movement, especially the movements of vehicles in a fictitious landscape. Two different tasks were also chosen. One was to predict where three vehicles will meet in the future given a visualization of their past movement history. The second task was to estimate the order in which four vehicles arrived at a specific place. Our results reveal that previous findings had generalized human perception in these situations and that large differences in user efficiency exist for a given task between different types of visualizations depicting the same data. Furthermore, our results are in line with earlier general findings on the nature of human perception of both object shape and scene changes. Finally, the need for new taxonomies of data and tasks based on results from perception research is discussed.

New technologies and techniques allow novel kinds of visualizations and different types of 3D visualizations are constantly developed. We propose a categorization of 3D visualizations and, based on this categorization, evaluate two versions of a space-time cube that show discrete spatiotemporal data. The two visualization techniques used are a head-tracked stereoscopic visualization (‘strong 3D’) and a static monocular visualization (‘weak 3D’). In terms of effectiveness and efficiency the weak 3D visualization is as good as the strong 3D and thus the need for advanced 3D visualizations in these kinds of tasks may not be necessary.

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Multi-Viewer Display Environments (MVDE) provide unique opportunities to present personalized information to several users concurrently in the same physical display space. MVDEs can support correct 3D visualizations to multiple users, present correctly oriented text and symbols to all viewers and allow individually chosen subsets of information in a shared context. MVDEs aim at supporting collaborative visual analysis, and when used to visualize disjoint information in partitioned visualizations they even necessitate collaboration. When solving visual tasks collaboratively in a MVDE, overall performance is affected not only by the inherent effects of the graphical presentation but also by the interaction between the collaborating users. We present results from an empirical study where we compared views with lack of shared visual references in disjoint sets of information to views with mutually shared information. Potential benefits of 2D and 3D visualizations in a collaborative task were investigated and the effects of partitioning visualizations both in terms of task performance, interaction behavior and clutter reduction. In our study of a collaborative task that required only a minimum of information to be shared, we found that partitioned views with a lack of shared visual references were significantly less efficient than integrated views. However, the study showed that subjects were equally capable of solving the task at low error levels in partitioned and integrated views. An explorative analysis revealed that the amount of visual clutter was reduced heavily in partitioned visualization, whereas verbal and deictic communication between subjects increased. It also showed that the type of the visualization (2D/3D) affects interaction behavior strongly. An interesting result is that collaboration on complex geo-time visualizations is actually as efficient in 2D as in 3D. Information Visualization (2010) 9, 98 --114.

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