Investigating time series visualisations to improve the user experience

Muhammad Adnan, Mike Just, Lynne Baillie

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Citations (Scopus)
338 Downloads (Pure)

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.
Original languageEnglish
Title of host publicationCHI'16: 2016 CHI Conference on Human Factors in Computing Systems Proceedings
PublisherAssociation for Computing Machinery
Pages5444-5455
Number of pages12
ISBN (Print)9781450333627
DOIs
Publication statusPublished - 7 May 2016
Event2016 CHI Conference on Human Factors in Computing Systems - San Jose, CA, United States
Duration: 7 May 201612 May 2016

Conference

Conference2016 CHI Conference on Human Factors in Computing Systems
Abbreviated titleCHI'16
CountryUnited States
CitySan Jose, CA
Period7/05/1612/05/16

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    Adnan, M., Just, M., & Baillie, L. (2016). Investigating time series visualisations to improve the user experience. In CHI'16: 2016 CHI Conference on Human Factors in Computing Systems Proceedings (pp. 5444-5455 ). Association for Computing Machinery. https://doi.org/10.1145/2858036.2858300