Evaluating 2D and 3D visualizations of spatiotemporal information

Abstract

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.

Evaluating the efficiency of physical visualizations

“…They are however difficult to avoid when the data is inherently 3D, as often in scientific visualization [6]. 3D information visualizations have also been shown to be useful in some tasks [30,20]. In addition, some of them are widely used and new techniques are regularly being proposed.…”

Improving Spatial Awareness for Human Trajectory Visualization in Space-Time Cubes

“…With the increasing advancements of computer graphics, 3D maps, and in particular space-time cubes (STCs) [8], have been proposed as viable options for the visualization of trajectories [12,10]. STCs represent both spatial and temporal information within a cube, where the x-y axes usually represent spatial information (e.g., latitude/longitude), while the z-axis represents time [8].…”

“…STCs represent both spatial and temporal information within a cube, where the x-y axes usually represent spatial information (e.g., latitude/longitude), while the z-axis represents time [8]. Typically, time increases along the z-axis, implying that the higher the information is within the cube, the most recent it is [10,1]. Similarly to 2D maps, trajectories can be displayed as a sequence of symbols, graphically encoded to represent variations in the thematic attributes.…”

“…Similarly to 2D maps, trajectories can be displayed as a sequence of symbols, graphically encoded to represent variations in the thematic attributes. However, since time is represented as a spatial position, other visual variables are available to represent the thematic attributes, when compared with traditional 2D maps [10]. STCs also allow the representation of various layers of information, each one defined as a plane in the z-axis [15], representing the state of an object in different moments in time.…”

“…Previous studies have shown that 3D views are not as effective as 2D alternatives in location/positioning based tasks [9,7]. To minimize these problems, previous studies suggested the use of interactive features, such as changing the point of view within the cube [10] or moving the plane representing spatial information up/downwards to facilitate locating objects in space and time [11]. As a result, while STCs can be considered as relevant tools for trajectory visualization, it is important to: (i) understand how to improve these techniques for spatiallyrelated tasks; and (ii) empirically validate the proposed features to help the interaction with STCs, taking into account the types of tasks a user might perform to achieve a given goal [14].…”