Data Visualization on Mobile Devices

Lee, Bongshin; Brehmer, Matthew; Isenberg, Petra; Choe, Eun Kyoung; Langner, Ricardo; Dachselt, Raimund

doi:10.1145/3170427.3170631

Cited by 26 publications

(18 citation statements)

References 13 publications

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“…Mirroring the rise of visualization on mobile devices, the body of research literature proposing and evaluating visual encoding and interaction techniques for mobile devices has also emerged in recent years [53], including efforts to establish a research agenda in this area [42]. This body of work spans different datatypes, including continuous time-series data [18], hierarchical data [3], and spatiotemporal data [39], as well as various application areas, from healthcare [19] to navigation [36].…”

Section: Visualization On Mobile Devicesmentioning

confidence: 99%

Visualizing Ranges over Time on Mobile Phones: A Task-Based Crowdsourced Evaluation

Brehmer

Lee

Isenberg

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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In the first crowdsourced visualization experiment conducted exclusively on mobile phones, we compare approaches to visualizing ranges over time on small displays. People routinely consume such data via a mobile phone, from temperatures in weather forecasting apps to sleep and blood pressure readings in personal health apps. However, we lack guidance on how to effectively visualize ranges on small displays in the context of different value retrieval and comparison tasks, or with respect to different data characteristics such as periodicity, seasonality, or the cardinality of ranges. Central to our experiment is a comparison between two ways to lay out ranges: a more conventional linear layout strikes a balance between quantitative and chronological scale resolution, while a less conventional radial layout emphasizes the cyclicality of time and may prioritize discrimination between values at its periphery. With results from 87 crowd workers, we found that while participants completed tasks more quickly with linear layouts than with radial ones, there were few differences in terms of error rate between layout conditions. We also found that participants performed similarly with both layouts in tasks that involved comparing superimposed observed and average ranges.

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Section: Visualization On Mobile Devicesmentioning

confidence: 99%

Visualizing Ranges over Time on Mobile Phones: A Task-Based Crowdsourced Evaluation

Brehmer

Lee

Isenberg

et al. 2019

IEEE Trans. Visual. Comput. Graphics

Self Cite

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“…Ideally, embedded visualizations are responsive, that is, they are able to adapt themselves automatically to external contextual requirements. The notion of responsive visualizations has mostly been discussed in the context of mobile visualization [14,44]. Recent research suggests that the design of a responsive visualization should consider layout, data density, and interaction-related aspects [3,4,31].…”

Section: Presentation Techniquesmentioning

confidence: 99%

Responsive Matrix Cells: A Focus+Context Approach for Exploring and Editing Multivariate Graphs

Horak

Berger

Schumann

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. Responsive matrix cells are a focus+context approach that provides details for a multivariate graph via embedded visualizations in a matrix representation and allows analysts to go from the overview in the matrix to details as well as editing within the cells. c b Abstract-Matrix visualizations are a useful tool to provide a general overview of a graph's structure. For multivariate graphs, a remaining challenge is to cope with the attributes that are associated with nodes and edges. Addressing this challenge, we propose responsive matrix cells as a focus+context approach for embedding additional interactive views into a matrix. Responsive matrix cells are local zoomable regions of interest that provide auxiliary data exploration and editing facilities for multivariate graphs. They behave responsively by adapting their visual contents to the cell location, the available display space, and the user task. Responsive matrix cells enable users to reveal details about the graph, compare node and edge attributes, and edit data values directly in a matrix without resorting to external views or tools. We report the general design considerations for responsive matrix cells covering the visual and interactive means necessary to support a seamless data exploration and editing. Responsive matrix cells have been implemented in a web-based prototype based on which we demonstrate the utility of our approach. We describe a walk-through for the use case of analyzing a graph of soccer players and report on insights from a preliminary user feedback session.

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“…Recently, there have been two efforts in establishing a community around mobile data visualization: (1) the "Data Visualization on Mobile Devices" workshop at CHI 2018 [2] (mobilevis.github.io) and (2) the "Mobile Data Visualization" Dagstuhl Seminar in July 2019 [11] (tinyurl.com/mobilevis-dagstuhl). We call for visualization researchers to attract researchers from related communities to advance this important and exciting topic.…”

Section: Data Visualization On Mobile Devicesmentioning

confidence: 99%

“…Published by the IEEE Computer Society c 2019 IEEE to make positive changes in their lives through better understanding of themselves [1]. The need and use of data visualization on mobile devices (e.g., tablets, smartphones, smartwatches) is increasingly prevalent in practice, enabling people to access data without relying on a desktop or laptop [2]. Advancements in both software and hardware technologies give us opportunities to provide people with disabilities with greater access to data visualization.…”

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confidence: 99%

Reaching Broader Audiences With Data Visualization

Lee

Choe

Isenberg

et al. 2020

IEEE Comput. Grap. Appl.

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The visualization research community can and should reach broader audiences beyond data-savvy groups of people, because these audiences could also greatly benefit from visual access to data. In this paper, we discuss four research topics-personal data visualization, data visualization on mobile devices, inclusive data visualization, and multimodal interaction for data visualization-that, individually and collaboratively, would help us reach broader audiences with data visualization, making data more accessible. VISUALIZATION research has become a burgeoning field encompassing many established and emerging sub areas, such as biological data visualization, visualization for cyber security, and visual analytics for healthcare, among others. Such research progression has empowered experts dealing with massive data (e.g., researchers, biologists, data analysts) to apply and leverage visualizations in their work. However, current visualization research often leaves out broader audiences, who could also benefit from innovations in visualization research. Compared to the

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Data Visualization on Mobile Devices

Cited by 26 publications

References 13 publications

Visualizing Ranges over Time on Mobile Phones: A Task-Based Crowdsourced Evaluation

Visualizing Ranges over Time on Mobile Phones: A Task-Based Crowdsourced Evaluation

Responsive Matrix Cells: A Focus+Context Approach for Exploring and Editing Multivariate Graphs

Reaching Broader Audiences With Data Visualization

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