Evaluating Pan and Zoom Timelines and Sliders

Schwab, Michail; Hao, Sicheng; Vitek, Olga; Tompkin, James; Huang, Jeff; Borkin, Michelle A.

doi:10.1145/3290605.3300786

Cited by 30 publications

(23 citation statements)

References 34 publications

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“…Toward a mobile visualization evaluation platform. We see this work as another step toward establishing and refining a mobile visualization evaluation methodology, one that incorporates and extends crowdsourced protocol used in previous work, such as in our recent experiment on alternative displays of ranges over time [17] or in Schwab et al's recent experiment on pan and zoom interactions across mobile and desktop devices [54]. Such a methodology will allow the visualization community to establish best practices and guidelines for visualizing data on mobile phones, which are undoubtedly needed as the prevalence of mobile-first and mobile-only data visualization continues to grow.…”

Section: Methodological Reflectionmentioning

confidence: 90%

“…Other recent research has considered novel interaction design choices for visualization on tablet and mobile devices (e.g., [8,24,34,35,54]). While we recognize the importance of identifying suitable mobile interaction design choices, we focus here on visual perception, comparing a static small multiples design with a non-interactive animated design.…”

Section: Visualization On Mobile Devicesmentioning

confidence: 99%

“…In response to a call for more empirical evidence to guide the design of visualization for mobile devices [11], researchers have recently started to take experimental approaches for mobile visualization [10,17,54]. Our experimental comparison of visualization design alternatives continues a tradition established by Cleveland and McGill [22] and continued by the crowdsourced graphical perception work of Heer and Bostock [31].…”

Section: (Mobile) Visualization Evaluation Studiesmentioning

confidence: 99%

“…Our experiment also makes use of a crowdsourcing platform, which serves to circumvent some of the limitations of directly observed lab studies while providing a diverse and large participant pool [12,13]. Our work follows two recent crowdsourced experiments relating to visualization on mobile phones: our previous study comparing alternative ways to visualize ranges on mobile phones [17], and Schwab et al's study of panning and zooming techniques with temporal data [54]. Our experiment adopts a similar protocol to the former, though in addition to different stimuli and tasks, we employed new techniques to improve the quality of responses.…”

Section: (Mobile) Visualization Evaluation Studiesmentioning

confidence: 99%

See 3 more Smart Citations

A Comparative Evaluation of Animation and Small Multiples for Trend Visualization on Mobile Phones

Brehmer

Lee

Isenberg

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. Cropped screen captures from our experimental mobile application. Left: an instance of the animation condition (open this PDF in Acrobat Reader to view the animation). Right: an instance of the small multiples condition, in which the position of each item reflects its values in the year 2000; a "trail" encodes the item's previous positions from 1975 to 1999.Abstract-We compare the efficacy of animated and small multiples variants of scatterplots on mobile phones for comparing trends in multivariate datasets. Visualization is increasingly prevalent in mobile applications and mobile-first websites, yet there is little prior visualization research dedicated to small displays. In this paper, we build upon previous experimental research carried out on larger displays that assessed animated and non-animated variants of scatterplots. Incorporating similar experimental stimuli and tasks, we conducted an experiment where 96 crowdworker participants performed nine trend comparison tasks using their mobile phones. We found that those using a small multiples design consistently completed tasks in less time, albeit with slightly less confidence than those using an animated design. The accuracy results were more task-dependent, and we further interpret our results according to the characteristics of the individual tasks, with a specific focus on the trajectories of target and distractor data items in each task. We identify cases that appear to favor either animation or small multiples, providing new questions for further experimental research and implications for visualization design on mobile devices. Lastly, we provide a reflection on our evaluation methodology.

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Section: Methodological Reflectionmentioning

confidence: 90%

Section: Visualization On Mobile Devicesmentioning

confidence: 99%

Section: (Mobile) Visualization Evaluation Studiesmentioning

confidence: 99%

Section: (Mobile) Visualization Evaluation Studiesmentioning

confidence: 99%

See 2 more Smart Citations

A Comparative Evaluation of Animation and Small Multiples for Trend Visualization on Mobile Phones

Brehmer

Lee

Isenberg

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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“…Previous work has evaluated the utility of various timeline visualizations for specific tasks. Schwab et al [30] evaluated how quickly users can navigate to a particular data point on a timeline using pan and zoom techniques.…”

Section: Related Workmentioning

confidence: 99%

Evaluating the Effect of Timeline Shape on Visualization Task Performance

Bartolomeo

Pandey

Leventidis

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

Self Cite

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SpiralCircle Vertical line Horizontal line Non-recurrentRecurrent Mixed Alignment experiment http://127.0.0.1:8000/test_questions.html Figure 1. We evaluate the effect on task performance of 4 timeline shapes (left to right) across 3 types of temporal event sequence data (top to bottom). These images are simplified versions of the stimuli that we used in our experiment. Each dot on a timeline represents an event and has a specific categorical color to highlight where the dataset has recurrent events. Dashed lines highlight the recurrent intervals or a set of recurrent events. ABSTRACTTimelines are commonly represented on a horizontal line, which is not necessarily the most effective way to visualize temporal event sequences. However, few experiments have evaluated how timeline shape influences task performance. We present the design and results of a controlled experiment run on Amazon Mechanical Turk (n = 192) in which we evaluate how timeline shape affects task completion time, correctness, and user preference. We tested 12 combinations of 4 shapeshorizontal line, vertical line, circle, and spiral -and 3 data types -recurrent, non-recurrent, and mixed event sequences. We found good evidence that timeline shape meaningfully affects user task completion time but not correctness and that users have a strong shape preference. Building on our results, we present design guidelines for creating effective timeline visualizations based on user task and data types. A free copy of this paper, the evaluation stimuli and data, and code are available at https://osf.io/qr5yu/

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