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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