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).


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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.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1109/MCG.2015.51
ISSNs: 0272-1716 (print)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Organisations: Agents, Interactions & Complexity
ePrint ID: 383924
Date :
Date Event
13 May 2015e-pub ahead of print
July 2015Published
Date Deposited: 11 Nov 2015 20:50
Last Modified: 17 Apr 2017 04:49
Further Information:Google Scholar

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