Abstract
The increasing popularity of smartphones and position tracking devices fostered the interest on human trajectory data analysis, and enabled the collection of large amounts of movement data. However, there are still several open challenges regarding the visual and interactive analysis of these data, as more people, often non-experienced, start using these methods. This paper presents a comparative study between two common visualization techniques for trajectory data analysis, namely a space-time cube and a static map, taking into consideration high-level visualization and analysis tasks. The results obtained with two similar prototypes support the adequacy of both methods for simple analysis tasks, and reveal that static maps are more adequate for locate tasks, while space-time cubes provide a better support for association tasks.
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@inproceedings{Goncalves:2014:VHT:2742941.2742967,
title = {Visualizing Human Trajectories: Comparing Space-Time Cubes and Static Maps},
author = {Tiago Gonçalves and Ana Paula Afonso and Bruno Martins},
url = {http://dx.doi.org/10.14236/ewic/hci2014.24},
doi = {10.14236/ewic/hci2014.24},
year = {2014},
date = {2014-09-10},
booktitle = {Proceedings of the 28th International BCS Human Computer Interaction Conference on HCI 2014 - Sand, Sea and Sky - Holiday HCI},
pages = {207--212},
publisher = {BCS},
address = {Southport, UK},
series = {BCS-HCI '14},
abstract = {The increasing popularity of smartphones and position tracking devices fostered the interest on human trajectory data analysis, and enabled the collection of large amounts of movement data. However, there are still several open challenges regarding the visual and interactive analysis of these data, as more people, often non-experienced, start using these methods. This paper presents a comparative study between two common visualization techniques for trajectory data analysis, namely a space-time cube and a static map, taking into consideration high-level visualization and analysis tasks. The results obtained with two similar prototypes support the adequacy of both methods for simple analysis tasks, and reveal that static maps are more adequate for locate tasks, while space-time cubes provide a better support for association tasks.},
keywords = {2D Map, Geographic Information Systems, Space-Time Cube, Trajectory Data, User Study, visual analytics},
pubstate = {published},
tppubtype = {inproceedings}
}