A Study on 2D and 3D Parallel Coordinates for Pattern Identification in Temporal Multivariate Data

Hassan, Kahin Akram; Rönnberg, Niklas; Forsell, Camilla; Cooper, Matthew; Johansson, Jimmy

doi:10.1109/iv.2019.00033

Cited by 7 publications

(2 citation statements)

References 27 publications

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“…Trajkova et al analyze relevant Twitter data and discuss facilitating data interpretation via visualization to avoid the spread of misconceptions and confusion on social media [72]. Also, multidimensional data visualization such as Parallel coordinates is commonly employed for visualizing multidimensional geometry [29,30,87,88]. They could apply visualization research on multidimensional attributes during the pandemic, to promote the understanding of how data entries compare to each other.…”

Section: Visualization In Epidemiological Analysismentioning

confidence: 99%

A Visual Modeling Method for Spatiotemporal and Multidimensional Features in Epidemiological Analysis: Applied COVID-19 Aggregated Datasets

Yu¹,

Liang²,

Chen³

et al. 2023

Preprint

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The visual modeling method enables flexible interactions with rich graphical depictions of data and supports the exploration of the complexities of epidemiological analysis. However, most epidemiology visualizations do not support the combined analysis of objective factors that might influence the transmission situation, resulting in a lack of quantitative and qualitative evidence. To address this issue, we have developed a portrait-based visual modeling method called +msRNAer. This method considers the spatiotemporal features of virus transmission patterns and the multidimensional features of objective risk factors in communities, enabling portrait-based exploration and comparison in epidemiological analysis. We applied +msRNAer to aggregate COVID-19-related datasets in New South Wales, Australia, which combined COVID-19 case number trends, geo-information, intervention events, and expert-supervised risk factors extracted from LGA-based censuses. We perfected the +msRNAer workflow with collaborative views and evaluated its feasibility, effectiveness, and usefulness through one user study and three subject-driven case studies. Positive feedback from experts indicates that +msRNAer provides a general understanding of analyzing comprehension that not only compares relationships between cases in time-varying and risk factors through portraits but also supports navigation in fundamental geographical, timeline, and other factor comparisons. By adopting interactions, experts discovered functional and practical implications for potential patterns of long-standing community factors against the vulnerability faced by the pandemic. Experts confirmed that +msRNAer is expected to deliver visual modeling benefits with spatiotemporal and multidimensional features in other epidemiological analysis scenarios.

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Section: Visualization In Epidemiological Analysismentioning

confidence: 99%

A Visual Modeling Method for Spatiotemporal and Multidimensional Features in Epidemiological Analysis: Applied COVID-19 Aggregated Datasets

Yu¹,

Liang²,

Chen³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The time dimension in such representation is either displayed on one of the axes or as the fourth dimension where users can investigate the three-dimensional data changing over time. Investigating temporal datasets with parallel coordinates in the three-dimensional space, where the time dimension is mapped to one of the axes, is common [2,41,94,111]. Although, the visualization community has long discussed whether the use of a three-dimensional space makes sense to present and explore two-dimensional abstract data [22].…”

Section: Multiple Axesmentioning

confidence: 99%

It’s About Time : User-centered Evaluation of Visual Representations for Temporal Data

Hassan

2021

Linköping Studies in Science and Technology. Dissertations

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I promise to achieve the life dreams you couldn't because you were too busy ensuring we would survive. To my dearest parents, thank you both for all your sacrifices. I am also incredibly happy that I had a whovian as my primary supervisor. Thank you, Niklas Rönnberg, for saving me at the eleventh hour and for the invaluable advice, continues support, and patience during my Ph.D. journey. Furthermore, I would like to express gratitude to Anders, Camilla, Lonni, and many others for their support and advice. Additionally, I would like to thank Alex, Fereshteh, Jochen, Sathish, Yu. But also Pier, for all the help and almost running me over with your car, Sergej et al. for a remarkable road-trip to Grand Canyon singing "I'm Gonna Be 500 Miles", Bara Kozlikova for the personality, hospitality, trips, and collaboration, I miss you, and who can forget, Gun-Britt, for all the administrative help! Also, a super special thank to my good friend Sophie and American friend and mentor Cory. Thank you for the unwavering support and belief in me from the start. Now to my 63.7% Greek friend Tony from either Northern England or South Scotland...? Without you, man, the three-week bartending course in Brooklyn would have sucked. My brother and I miss you, hope to see you soon. Thank you Sajad, for trying to teach me not to give a damn, Admir for all the memorable days together, such as kayaking from Norrköping to Västervik but also for always talking about the stock market, and Marcus, Ludvig, Alan, and Nils for all the fun times and events we planned together.Finally, there are soooo many series and characters to thank. I could go on and on about how much time I have "wasted" getting lost in fictional stories. But who are we to say anything when time just is a big ball of wibbly-wobbly, timey-wimey ... stuff.

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Toward a taxonomy for 2D non-paired General Line Coordinates: a comprehensive survey

Antonini

Luque

Ganuza

et al. 2022

Int J Data Sci Anal

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A Study on 2D and 3D Parallel Coordinates for Pattern Identification in Temporal Multivariate Data

Cited by 7 publications

References 27 publications

A Visual Modeling Method for Spatiotemporal and Multidimensional Features in Epidemiological Analysis: Applied COVID-19 Aggregated Datasets

A Visual Modeling Method for Spatiotemporal and Multidimensional Features in Epidemiological Analysis: Applied COVID-19 Aggregated Datasets

It’s About Time : User-centered Evaluation of Visual Representations for Temporal Data

Toward a taxonomy for 2D non-paired General Line Coordinates: a comprehensive survey

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