Paper Info
Title
Characterizing Visualization Insights from Quantified Selfers' Personal Data Presentations
Abstract
AbstractData 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.
Year
DOI
Venue
2015
10.1109/MCG.2015.51
Periodicals
Field
DocType
Volume
Data science,Data visualization,Computer science,Visualization,Outlier,Context model,Analytics,Computer graphics,Empirical research,Market research
Journal
35
Issue
ISSN
Citations 
4
0272-1716
16
PageRank 
References 
Authors
0.66
15
3
Name
Order
Citations
PageRank
Eun Kyoung Choe151838.00
Bongshin Lee22738143.95
M. C. Schraefel3116085.15