Visualizing Personal Progress in Participatory Sports Cycling Events

Wood, Jo

doi:10.1109/mcg.2015.71

Cited by 13 publications

(15 citation statements)

References 3 publications

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“…As a result, with few exceptions (e.g., [PBV16, TSLR07, PYHZ14]), most of the research papers we surveyed do not contribute a new generic technique but instead a tailored adaptation of an existing one. The most frequent visualization technique extensions we found were of node‐link graphs (e.g., [PVF13b]), heatmaps (e.g., [PSBS12, Gol12, Bes16, AAB*16, MB13]), small multiples and glyphs (e.g., [HZ16, LCP*12, CPG*16, CF13, PYHZ14], treemaps (e.g., [JB97, CS06, Tur94], arc diagrams (e.g., [OJK13]), parallel coordinates (e.g., [OJK13, CPG*16], and line charts (e.g., [Woo15, PBV16, Won16]). For the same reasons, sports data visualization is also propitious to the design of new interaction techniques .…”

Section: Discussion and Perspectivesmentioning

confidence: 99%

“…In the context of cyclist group racing, Wood created graphs (such as the one shown in Figure ) showing the relative difference between each cyclist and the top performer at each point in time of the race [Woo15].…”

Section: Visualization By Type: Box‐score Datamentioning

confidence: 99%

“…Relative time differences between each cyclist participating in the race and the top performer at that moment [Woo15]. Red lines encode cyclists who did not finish the race.…”

Section: Visualization By Type: Box‐score Datamentioning

confidence: 99%

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State of the Art of Sports Data Visualization

Périn

Vuillemot

Stolper

et al. 2018

Computer Graphics Forum

106

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In this report, we organize and reflect on recent advances and challenges in the field of sports data visualization. The exponentially-growing body of visualization research based on sports data is a prime indication of the importance and timeliness of this report. Sports data visualization research encompasses the breadth of visualization tasks and goals: exploring the design of new visualization techniques; adapting existing visualizations to a novel domain; and conducting design studies and evaluations in close collaboration with experts, including practitioners, enthusiasts, and journalists. Frequently this research has impact beyond sports in both academia and in industry because it is i) grounded in realistic, highly heterogeneous data, ii) applied to real-world problems, and iii) designed in close collaboration with domain experts. In this report, we analyze current research contributions through the lens of three categories of sports data: box score data (data containing statistical summaries of a sport event such as a game), tracking data (data about in-game actions and trajectories), and meta-data (data about the sport and its participants but not necessarily a given game). We conclude this report with a high-level discussion of sports visualization research informed by our analysis-identifying critical research gaps and valuable opportunities for the visualization community. More information is available at the STAR's website: https://sportsdataviz.github.io/.

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Section: Discussion and Perspectivesmentioning

confidence: 99%

Section: Visualization By Type: Box‐score Datamentioning

confidence: 99%

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State of the Art of Sports Data Visualization

Périn

Vuillemot

Stolper

et al. 2018

Computer Graphics Forum

106

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“…With toll gates on highways and with check-in gates of subways, we know the direction and precise count of entities accessing a particular area; with cameras this is less precise. Previous research on traffic management in combination with checkpoints concentrated on toll gate placement and pricing [7,11,28], travel time estimation [3,26], or traffic flow modeling in general, see, for example, [12].…”

Section: Modeling and Computation Of Flow In Specific Casesmentioning

confidence: 99%

“…Recently, a different type of movement data has become of increasing interest, namely checkpoint data [3,4], [6], [11], [25,26], [28]. Here the entities themselves need not be equipped with GPS, but rather their presence at a location or neighborhood is recorded by a stationary sensor.…”

Section: Introductionmentioning

confidence: 99%

Modeling Checkpoint-Based Movement with the Earth Mover’s Distance

Duckham

Kreveld

Purves

et al. 2016

Geographic Information Science

Self Cite

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract. Movement data comes in various forms, including trajectory data and checkpoint data. While trajectories give detailed information about the movement of individual entities, checkpoint data in its simplest form does not give identities, just counts at checkpoints. However, checkpoint data is of increasing interest since it is readily available due to privacy reasons and as a by-product of other data collection. In this paper we propose to use the Earth Mover's Distance as a versatile tool to reconstruct individual movements or flow based on checkpoint counts at different times. We analyze the modeling possibilities and provide experiments that validate model predictions, based on coarse-grained aggregations of data about actual movements of couriers in London, UK. While we cannot expect to reconstruct precise individual movements from highly granular checkpoint data, the evaluation does show that the approach can generate meaningful estimates of object movements. Permanent repository link

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A survey of competitive sports data visualization and visual analysis

Yuan

2020

J Vis

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Visualizing Personal Progress in Participatory Sports Cycling Events

Abstract: This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link:

Cited by 13 publications

References 3 publications

State of the Art of Sports Data Visualization

State of the Art of Sports Data Visualization

Modeling Checkpoint-Based Movement with the Earth Mover’s Distance

A survey of competitive sports data visualization and visual analysis

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