Why not both? – Combining 2D maps and 3D space-time cubes for human trajectory data visualization

Abstract

Throughout the years, researchers have tried to understand dynamics and general patterns associated with human movement, e.g. in the context of urban planning, to improve the lives of citizens. To do this, it is necessary to properly analyse the spatio-temporal and the thematic properties of their trajectory data. Thematic maps, particularly 2D maps and 3D space-time cubes (STCs), are among the most common approaches to analyse and visualize these data. Previous research attests to the usefulness of these visualization techniques in different types of tasks. However, it is unlikely that the analysis of trajectories will be always limited to a specific type of task, thus, motivating additional studies to evaluate the dis/advantages of combining both techniques, within the same view/interaction context. In this paper, we address this specific challenge, by presenting a comparative study between three prototypes, one using a 2D map, one using a STC, and one combining both techniques, for the visualization of trajectories. Our results support previous studies' conclusions, by showing the advantages of 2D maps and STCs for spatial and spatio-temporal tasks, respectively. They also point out towards the advantages of using both techniques together, as users seem to prefer this alternative and are able to complete different types of tasks accurately, despite the increasing complexity of the visualization.

Integrated Visualization of Space and Time: A Distributed Cognition Perspective

“…Users of CMV had to interact more with the time slider to answer these questions resulting in longer time on spatiotemporal tasks. Overall, studies confirm that CMV are better suited for elementary tasks and STC for complex, spatiotemporal tasks [43], [44]. STC representations seem to perform worse if the "metric properties of a visualization" have to be perceived [40, p. 20].…”

“…Overall, the task performance with different spatiotemporal visualization techniques strongly depends on the tasks and data used [48]. Consequently, a newer group of studies does not study individual spatiotemporal visualizations, but allows users to select the ones which seem most appropriate for the current task [49] or combines them in a coordinated manner [44], [50]- [52]. Users more often turned to the STC in a complex dataset, and to a static map in a simpler data set [51].…”

COVID-19 geoviz for spatio-temporal structures detection

“…The space-time cube (STC) is a 3D visualization technique that maps geographical positions in the x and y coordinates and time vertically (Hägerstraand et al, 1970). The spacetime cube has been used to visualize trajectories (Kraak, 2008, Gonc ¸alves et al, 2016, discrete events such as earthquakes (Gatalsky et al, 2004), crime events and disease data (Kraak, 2008, , n.d.), and in particular to visualize COVID data (Lan et al, 2021). Because plotting many events might result in occlusion, some approaches explore aggregating them spatially or temporally and using an alternative representation such as density-based representations (Demšar et al, 2015) or helix representations (League and Kennelly, 2019).…”

Evaluating an Immersive Space-Time Cube Geovisualization for Intuitive Trajectory Data Exploration

“…Gonc ¸alves et al [18] advocated the combined usage of the STC and 2D maps, since the analysis of trajectories is not limited to a single type of task. In user studies involving between 16 and 30 participants, they found that the 2D map is always faster and more accurate for location tasks, while the STC performs best for association tasks [17].…”

“…Further, we also plan to expand our immersive environment to include complementary data representations and support more task categories [18,31]. We might also investigate additional information encodings, such as varying tube radii, and the integration of desktopbased and immersive implementations, to allow seamless transitions, according to the analyst's needs [15].…”