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Revealing Visualization Insights from Quantified-Selfers' Personal Data Presentations.

Revealing Visualization Insights from Quantified-Selfers' Personal Data Presentations.
Revealing 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 are neither visualization experts nor data scientists. Consequently, their visualizations of their data are often not ideal for conveying their insights. Aiming to design a visualization system to help non-experts explore and present their personal data, we conducted a pre-design empirical study. Through the lens of Quantified-Selfers, we examined what insights people gain specifically from their personal data and how they use visualizations to communicate their insights. Based on our analysis of 30 Quantified Self presentations, we characterized eight insight types (detail, self-reflection, trend, comparison, correlation, data summary, distribution, outlier) and mapped the visual annotations used to communicate them. We 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.
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
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) Revealing Visualization Insights from Quantified-Selfers' Personal Data Presentations. IEEE Computer Graphics and Applications

Record type: Article

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 are neither visualization experts nor data scientists. Consequently, their visualizations of their data are often not ideal for conveying their insights. Aiming to design a visualization system to help non-experts explore and present their personal data, we conducted a pre-design empirical study. Through the lens of Quantified-Selfers, we examined what insights people gain specifically from their personal data and how they use visualizations to communicate their insights. Based on our analysis of 30 Quantified Self presentations, we characterized eight insight types (detail, self-reflection, trend, comparison, correlation, data summary, distribution, outlier) and mapped the visual annotations used to communicate them. We 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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Published date: May 2015
Organisations: Agents, Interactions & Complexity

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Local EPrints ID: 405295
URI: http://eprints.soton.ac.uk/id/eprint/405295
PURE UUID: 8e6be94c-cce8-4d0f-82be-0fcb38b48b9d

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Date deposited: 31 Jan 2017 12:15
Last modified: 24 Jul 2017 16:36

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Contributors

Author: Eun Kyoung Choe
Author: Bongshin Lee
Author: m.c. schraefel

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