Data for: Assessing the Cognition of Movement Trajectory Visualizations: Interpreting Speed and Direction

This paper evaluates cognitively plausible geovisualization techniques for mapping movement data. With the widespread increase in the availability and quality of space-time data capturing movement trajectories of individuals, meaningful representations are needed to properly visualize and communicate trajectory data and complex movement patterns using geographic displays. Many visualization and visual analytics approaches have been proposed to map movement trajectories (e.g. space-time paths, animations, trajectory lines, etc.). However, little is known about how effective these complex visualizations are in capturing important aspects of movement data. Given the complexity of movement data which involves space, time, and context dimensions, it is essential to evaluate the communicative efficiency and efficacy of various visualization forms in helping people understand movement data. This study assesses the effectiveness of static and dynamic movement displays as well as visual variable..., The movement visualization files used in the study were generated using the DynamoVis desktop software, available on Github: https://github.com/move-ucsb/DynamoVis
Static visualizations were generated as exported screenshots from the software. Dynamic visualizations were generated as exported videos from the software (animated screenshots). Both types of visualizations were created using the built-in export features of DynamoVis. After export, images and videos were edited to add further contextual information, including start and stop icons on the static images, as well as scale bars on all visualizations for contextual information.
The survey study design and data collection and analysis methods are described in the associated manuscript. A copy of the survey instrument and an anonymized survey report are included in the data folder.
 
 , Please refer to the README.txt file.