  A Space-Time Cube enables analysts to clearly observe spatio-temporal
features in movement trajectory datasets in geovisualization. However, its
general usability is impacted by a lack of depth cues, a reported steep
learning curve, and the requirement for efficient 3D navigation. In this work,
we investigate a Space-Time Cube in the Immersive Analytics domain. Based on a
review of previous work and selecting an appropriate exploration metaphor, we
built a prototype environment where the cube is coupled to a virtual
representation of the analyst's real desk, and zooming and panning in space and
time are intuitively controlled using mid-air gestures. We compared our
immersive environment to a desktop-based implementation in a user study with 20
participants across 7 tasks of varying difficulty, which targeted different
user interface features. To investigate how performance is affected in the
presence of clutter, we explored two scenarios with different numbers of
trajectories. While the quantitative performance was similar for the majority
of tasks, large differences appear when we analyze the patterns of interaction
and consider subjective metrics. The immersive version of the Space-Time Cube
received higher usability scores, much higher user preference, and was rated to
have a lower mental workload, without causing participants discomfort in
25-minute-long VR sessions.
