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Characterizing visualization insights from quantified selfers' personal data presentations

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), pp. 28-37. (doi:10.1109/MCG.2015.51). (PMID:25974930).

Record type: Article


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


Local EPrints ID: 383924
ISSN: 0272-1716
PURE UUID: 871ef705-c822-42e0-be3c-ba88b47475ca

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Date deposited: 11 Nov 2015 20:50
Last modified: 17 Jul 2017 20:09

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

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