City Research Online

Abstract

Trajectory-based visualization of coordinated movement data within a bounded area, such as player and ball movement within a soccer pitch, can easily result in visual crossings, overplotting, and clutter. Trajectory abstraction can help to cope with these issues, but it is a challenging problem to select the right level of abstraction (LoA) for a given data set and analysis task. We present a novel dynamic approach that combines trajectory simplification and clustering techniques with the goal to support interpretation and understanding of movement patterns. Our technique provides smooth transitions between different abstraction types that can be computed dynamically and on-the-fly. This enables the analyst to effectively navigate and explore the space of possible abstractions in large trajectory data sets. Additionally, we provide a proof of concept for supporting the analyst in determining the LoA semi-automatically with a recommender system. Our approach is illustrated and evaluated by case studies, quantitative measures, and expert feedback. We further demonstrate that it allows analysts to solve a variety of analysis tasks in the domain of soccer.

Publication Type:	Article
Additional Information:	This is the peer reviewed version of the following article: Sacha, D., Al-Masoudi, F., Stein, M., Schreck, T., Keim, D. A., Andrienko, G. and Janetzko, H. (2017), Dynamic Visual Abstraction of Soccer Movement. Computer Graphics Forum, 36: 305–315., which has been published in final form at http://dx.doi.org/10.1111/cgf.13189. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	School of Science & Technology > Computer Science School of Science & Technology > Computer Science > giCentre
SWORD Depositor:	Symplectic Administrator

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