Characterizing visualization insights from quantified selfers' personal data presentations
Characterizing visualization insights from quantified selfers' personal data presentations
Data visualization and analytics research has great potential to empower people to improve their lives by leveraging their own personal data. However, most quantified selfers (Q-Selfers) are neither visualization experts nor data scientists. Consequently, visualizations Q-Selfers created with their data are often not ideal for conveying insights. Aiming to design a visualization system to help nonexperts gain and communicate personal data insights, the authors conducted a predesign empirical study. Through the lens of Q-Selfers, they examined what insights people gain specifically from their personal data and how they use visualizations to communicate their insights. Based on their analysis of 30 quantified self-presentations, they characterized eight insight types (detail, self-reflection, trend, comparison, correlation, data summary, distribution, and outlier) and mapped the visual annotations used to communicate them. They further discussed four areas for the design of personal visualization systems, including support for encouraging self-reflection, gaining valid insight, communicating insight, and using visual annotations.
28-37
Choe, Eun Kyoung
4372202a-6802-4f4a-89b1-328d6ff76c74
Lee, Bongshin
c126226e-876d-44b2-81fd-948298efe5b9
schraefel, m.c.
ac304659-1692-47f6-b892-15113b8c929f
July 2015
Choe, Eun Kyoung
4372202a-6802-4f4a-89b1-328d6ff76c74
Lee, Bongshin
c126226e-876d-44b2-81fd-948298efe5b9
schraefel, m.c.
ac304659-1692-47f6-b892-15113b8c929f
Choe, Eun Kyoung, Lee, Bongshin and schraefel, m.c.
(2015)
Characterizing visualization insights from quantified selfers' personal data presentations.
[in special issue: Personal Visualisation and Personal Visual Analytics]
IEEE Computer Graphics and Applications, 35 (4), .
(doi:10.1109/MCG.2015.51).
(PMID:25974930)
Abstract
Data visualization and analytics research has great potential to empower people to improve their lives by leveraging their own personal data. However, most quantified selfers (Q-Selfers) are neither visualization experts nor data scientists. Consequently, visualizations Q-Selfers created with their data are often not ideal for conveying insights. Aiming to design a visualization system to help nonexperts gain and communicate personal data insights, the authors conducted a predesign empirical study. Through the lens of Q-Selfers, they examined what insights people gain specifically from their personal data and how they use visualizations to communicate their insights. Based on their analysis of 30 quantified self-presentations, they characterized eight insight types (detail, self-reflection, trend, comparison, correlation, data summary, distribution, and outlier) and mapped the visual annotations used to communicate them. They further discussed four areas for the design of personal visualization systems, including support for encouraging self-reflection, gaining valid insight, communicating insight, and using visual annotations.
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e-pub ahead of print date: 13 May 2015
Published date: July 2015
Organisations:
Agents, Interactions & Complexity
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Local EPrints ID: 383924
URI: http://eprints.soton.ac.uk/id/eprint/383924
ISSN: 0272-1716
PURE UUID: 871ef705-c822-42e0-be3c-ba88b47475ca
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Date deposited: 11 Nov 2015 20:50
Last modified: 15 Mar 2024 03:16
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Author:
Eun Kyoung Choe
Author:
Bongshin Lee
Author:
m.c. schraefel
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