Space Transformation for Understanding Group Movement

Andrienko, Natalia; Barrett, Louise; Dostie, Marcus J.; Henzi, S. Peter

doi:10.1109/tvcg.2013.193

Cited by 55 publications

(35 citation statements)

References 30 publications

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“…Andrienko et al [43] suggest analyzing movement at two different granularities, of the individuals and of the group as a whole. Nevertheless, we also have to deal with pure time-series or time-stamped event data and with spatial topology data.…”

Section: Abstracting the Data Spacementioning

confidence: 99%

How to Make Sense of Team Sport Data: From Acquisition to Data Modeling and Research Aspects

Stein

Janetzko

Seebacher

et al. 2017

Data

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Automatic and interactive data analysis is instrumental in making use of increasing amounts of complex data. Owing to novel sensor modalities, analysis of data generated in professional team sport leagues such as soccer, baseball, and basketball has recently become of concern, with potentially high commercial and research interest. The analysis of team ball games can serve many goals, e.g., in coaching to understand effects of strategies and tactics, or to derive insights improving performance. Also, it is often decisive to trainers and analysts to understand why a certain movement of a player or groups of players happened, and what the respective influencing factors are. We consider team sport as group movement including collaboration and competition of individuals following specific rule sets. Analyzing team sports is a challenging problem as it involves joint understanding of heterogeneous data perspectives, including high-dimensional, video, and movement data, as well as considering team behavior and rules (constraints) given in the particular team sport. We identify important components of team sport data, exemplified by the soccer case, and explain how to analyze team sport data in general. We identify challenges arising when facing these data sets and we propose a multi-facet view and analysis including pattern detection, context-aware analysis, and visual explanation. We also present applicable methods and technologies covering the heterogeneous aspects in team sport data.

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Section: Abstracting the Data Spacementioning

confidence: 99%

How to Make Sense of Team Sport Data: From Acquisition to Data Modeling and Research Aspects

Stein

Janetzko

Seebacher

et al. 2017

Data

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“…When trajectories represent simultaneous movement of a group of objects, the points from different trajectories can be grouped based on common time references [15,49], which is not applicable when trajectories are not synchronous. Lee et al [40] propose a time-agnostic method suitable for clusters of sub-trajectories represented by straight line segments.…”

Section: Summarization Of Clusters Of Trajectoriesmentioning

confidence: 99%

Clustering Trajectories by Relevant Parts for Air Traffic Analysis

Andrienko

Fuchs

et al. 2018

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. Different approach routes to London airports are represented in a 3D view by central trajectories of density-based clusters of relevant parts of flight trajectories. The images show the routes that were used in two days with different wind parameters.Abstract-Clustering of trajectories of moving objects by similarity is an important technique in movement analysis. Existing distance functions assess the similarity between trajectories based on properties of the trajectory points or segments. The properties may include the spatial positions, times, and thematic attributes. There may be a need to focus the analysis on certain parts of trajectories, i.e., points and segments that have particular properties. According to the analysis focus, the analyst may need to cluster trajectories by similarity of their relevant parts only. Throughout the analysis process, the focus may change, and different parts of trajectories may become relevant. We propose an analytical workflow in which interactive filtering tools are used to attach relevance flags to elements of trajectories, clustering is done using a distance function that ignores irrelevant elements, and the resulting clusters are summarized for further analysis. We demonstrate how this workflow can be useful for different analysis tasks in three case studies with real data from the domain of air traffic. We propose a suite of generic techniques and visualization guidelines to support movement data analysis by means of relevance-aware trajectory clustering.

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“…Pattern extraction techniques present extracted patterns of movement to analysts for interpretation and further investigation. Many patterns, such as the interchange pattern [31] and the group movement pattern [32], have been studied. In our paper, we focus on spatial temporal exploration of large-scale check-in data of urban dimensions, including location, activity, and user data and to provide interactive geographic visualization for similarity exploration and pattern extraction in spatial temporal data.…”

Section: Related Workmentioning

confidence: 99%

Sci-Fin: Visual Mining Spatial and Temporal Behavior Features from Social Media

Teng

Gong

et al. 2016

Sensors

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Check-in records are usually available in social services, which offer us the opportunity to capture and analyze users’ spatial and temporal behaviors. Mining such behavior features is essential to social analysis and business intelligence. However, the complexity and incompleteness of check-in records bring challenges to achieve such a task. Different from the previous work on social behavior analysis, in this paper, we present a visual analytics system, Social Check-in Fingerprinting (Sci-Fin), to facilitate the analysis and visualization of social check-in data. We focus on three major components of user check-in data: location, activity, and profile. Visual fingerprints for location, activity, and profile are designed to intuitively represent the high-dimensional attributes. To visually mine and demonstrate the behavior features, we integrate WorldMapper and Voronoi Treemap into our glyph-like designs. Such visual fingerprint designs offer us the opportunity to summarize the interesting features and patterns from different check-in locations, activities and users (groups). We demonstrate the effectiveness and usability of our system by conducting extensive case studies on real check-in data collected from a popular microblogging service. Interesting findings are reported and discussed at last.

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Space Transformation for Understanding Group Movement

Cited by 55 publications

References 30 publications

How to Make Sense of Team Sport Data: From Acquisition to Data Modeling and Research Aspects

How to Make Sense of Team Sport Data: From Acquisition to Data Modeling and Research Aspects

Clustering Trajectories by Relevant Parts for Air Traffic Analysis

Sci-Fin: Visual Mining Spatial and Temporal Behavior Features from Social Media

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