Time sequences

Ross Shannon; Aaron Quigley; Paddy Nixon

doi:10.1145/1520340.1520709

Time sequences

Ross Shannon^*, Aaron Quigley, Paddy Nixon

^*Corresponding author for this work

School of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Visualisations of dynamic data change in appearance over time, reflecting changes in the underlying data, be that the development of a social network, or the addition or removal of a device node in an ad-hoc communications network. As viewers of these visualisation tools, it is up to us to accurately perceive and keep up with the constantly shifting view, mentally noting as visual elements are added, removed, changed and rearranged, sometimes at great pace. In a complex data set with a lot happening, this can be a strain on the observer's comprehension, with changes in layout and visual population disrupting their internalised "mental model" of the data, leading to errors in perception. We present Time Sequences, a novel dual visualisation technique which dilates the flow of time in the visualisation so that observers are given proportionally more time to understand changes based on the density of activity in the visualisation.

Original language	English
Title of host publication	Proceedings of the 27th International Conference Extended Abstracts on Human Factors in Computing Systems, CHI 2009
Pages	4615-4620
Number of pages	6
DOIs	https://doi.org/10.1145/1520340.1520709
Publication status	Published - 22 Sept 2009
Event	27th International Conference Extended Abstracts on Human Factors in Computing Systems, CHI 2009 - Boston, MA, United States Duration: 4 Apr 2009 → 9 Apr 2009

Conference

Conference	27th International Conference Extended Abstracts on Human Factors in Computing Systems, CHI 2009
Country/Territory	United States
City	Boston, MA
Period	4/04/09 → 9/04/09

Keywords

Dynamic data
Human factors
Perception
Visual analytics
Visualization

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10.1145/1520340.1520709

Cite this

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title = "Time sequences",

abstract = "Visualisations of dynamic data change in appearance over time, reflecting changes in the underlying data, be that the development of a social network, or the addition or removal of a device node in an ad-hoc communications network. As viewers of these visualisation tools, it is up to us to accurately perceive and keep up with the constantly shifting view, mentally noting as visual elements are added, removed, changed and rearranged, sometimes at great pace. In a complex data set with a lot happening, this can be a strain on the observer's comprehension, with changes in layout and visual population disrupting their internalised {"}mental model{"} of the data, leading to errors in perception. We present Time Sequences, a novel dual visualisation technique which dilates the flow of time in the visualisation so that observers are given proportionally more time to understand changes based on the density of activity in the visualisation.",

keywords = "Dynamic data, Human factors, Perception, Visual analytics, Visualization",

author = "Ross Shannon and Aaron Quigley and Paddy Nixon",

year = "2009",

month = sep,

day = "22",

doi = "10.1145/1520340.1520709",

language = "English",

isbn = "9781605582474",

pages = "4615--4620",

booktitle = "Proceedings of the 27th International Conference Extended Abstracts on Human Factors in Computing Systems, CHI 2009",

note = "27th International Conference Extended Abstracts on Human Factors in Computing Systems, CHI 2009 ; Conference date: 04-04-2009 Through 09-04-2009",

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AU - Shannon, Ross

AU - Quigley, Aaron

AU - Nixon, Paddy

PY - 2009/9/22

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N2 - Visualisations of dynamic data change in appearance over time, reflecting changes in the underlying data, be that the development of a social network, or the addition or removal of a device node in an ad-hoc communications network. As viewers of these visualisation tools, it is up to us to accurately perceive and keep up with the constantly shifting view, mentally noting as visual elements are added, removed, changed and rearranged, sometimes at great pace. In a complex data set with a lot happening, this can be a strain on the observer's comprehension, with changes in layout and visual population disrupting their internalised "mental model" of the data, leading to errors in perception. We present Time Sequences, a novel dual visualisation technique which dilates the flow of time in the visualisation so that observers are given proportionally more time to understand changes based on the density of activity in the visualisation.

AB - Visualisations of dynamic data change in appearance over time, reflecting changes in the underlying data, be that the development of a social network, or the addition or removal of a device node in an ad-hoc communications network. As viewers of these visualisation tools, it is up to us to accurately perceive and keep up with the constantly shifting view, mentally noting as visual elements are added, removed, changed and rearranged, sometimes at great pace. In a complex data set with a lot happening, this can be a strain on the observer's comprehension, with changes in layout and visual population disrupting their internalised "mental model" of the data, leading to errors in perception. We present Time Sequences, a novel dual visualisation technique which dilates the flow of time in the visualisation so that observers are given proportionally more time to understand changes based on the density of activity in the visualisation.

KW - Dynamic data

KW - Human factors

KW - Perception

KW - Visual analytics

KW - Visualization

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DO - 10.1145/1520340.1520709

M3 - Conference contribution

AN - SCOPUS:70349168257

SN - 9781605582474

SP - 4615

EP - 4620

BT - Proceedings of the 27th International Conference Extended Abstracts on Human Factors in Computing Systems, CHI 2009

T2 - 27th International Conference Extended Abstracts on Human Factors in Computing Systems, CHI 2009

Y2 - 4 April 2009 through 9 April 2009

ER -

Time sequences

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Conference

Keywords

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