research-article

Investigating Time Series Visualisations to Improve the User Experience

Authors:

Muhammad Adnan,

Lynne BaillieAuthors Info & Claims

CHI '16: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

Pages 5444 - 5455

https://doi.org/10.1145/2858036.2858300

Published: 07 May 2016 Publication History

Abstract

Research on graphical perception of time series visualisations has focused on visual representation, and not on interaction. Even for visual representation, there has been limited study of the impact on users of visual encodings and the strengths and weaknesses of Cartesian and Polar coordinate systems. In order to address this research gap, we performed a comprehensive graphical perception study that measured the effectiveness of time series visualisations with different interactions, visual encodings and coordinate systems for several tasks. Our results show that, while positional and colour visual encodings were better for most tasks, area visual encoding performed better for data comparison. Most importantly, we identified that introducing interactivity within time series visualisations considerably enhances the user experience, without any loss of efficiency or accuracy. We believe that our findings can greatly improve the development of visual analytics tools using time series visualisations in a variety of domains.

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Braun DBorgo RSondag Mvon Landesberger T(2024)Reclaiming the Horizon: Novel Visualization Designs for Time-Series Data with Large Value RangesIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332657630:1(1161-1171)Online publication date: 1-Jan-2024
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Index Terms

Investigating Time Series Visualisations to Improve the User Experience
1. Human-centered computing
  1. Human computer interaction (HCI)

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cover image ACM Conferences

CHI '16: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

May 2016

6108 pages

ISBN:9781450333627

DOI:10.1145/2858036

General Chairs:
Jofish Kaye
Yahoo
,
Allison Druin
University of Maryland / National Park Service
,
Program Chairs:
Cliff Lampe
University of Michigan
,
Dan Morris
Microsoft
,
Juan Pablo Hourcade
University of Iowa

Copyright © 2016 ACM.

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Published: 07 May 2016

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https://dl.acm.org/doi/10.1109/TVCG.2023.3326589
Braun DBorgo RSondag Mvon Landesberger T(2024)Reclaiming the Horizon: Novel Visualization Designs for Time-Series Data with Large Value RangesIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.332657630:1(1161-1171)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TVCG.2023.3326576
Chen HBeachnau AThomas PManeriker PKimball JRossi R(2024)LIVE-ITS: LSH-based Interactive Visualization Explorer for Large-Scale Incomplete Time Series2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825096(840-849)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825096
Guo JZhang HYe XWang HYang YTang G(2024)Spatial association measures for time series with fixed spatial locationsInternational Journal of Geographical Information Science10.1080/13658816.2024.2445185(1-25)Online publication date: 26-Dec-2024
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Svalina APibernik JDolić JMandić L(2023)Assessing the Design of Interactive Radial Data Visualizations for Mobile DevicesJournal of Imaging10.3390/jimaging90501009:5(100)Online publication date: 14-May-2023
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Alves TDias CGoncalves DGama S(2022)Exploring how Temporal Framing Affects Trust with Time-series Visualizations2022 International Conference on Graphics and Interaction (ICGI)10.1109/ICGI57174.2022.9990428(1-8)Online publication date: 3-Nov-2022
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Schwab MPandey ABorkin M(2021)Evaluation of 1D Selection Techniques for Mobile VisualizationsExtended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411763.3451650(1-7)Online publication date: 8-May-2021
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