Author:
Choe, Eun Kyoung
Lee, Bongshin
Schraefel, M.c.
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.
Document:
https://ieeexplore.ieee.org/document/7106391
References
1. E.K. Choe et al., “Understanding Quantified-Selfers’ Practices in Collecting and Exploring Personal Data”, Proc. SIGCHI Conf. Human Factors in Computing Systems, pp. 1143-1152, 2014. Show Context Access at ACM Google Scholar
2. D. Huang et al., “Personal Visualization and Personal Visual Analytics”, IEEE Trans. Visualization and Computer Graphics, vol. 21, no. 3, pp. 420-433, 2015. Show Context View Article Full Text: PDF (634KB) Google Scholar
3. L. Grammel, M. Tory and M. Storey, “How Information Visualization Novices Construct Visualizations”, IEEE Trans. Visualization and Computer Graphics, vol. 16, no. 6, pp. 943-952, 2010. Show Context View Article Full Text: PDF (2129KB) Google Scholar
4. R. Chang et al., “Defining Insight for Visual Analytics”, IEEE Computer Graphics and Applications, vol. 29, no. 2, pp. 14-17, 2009. Show Context View Article Full Text: PDF (539KB) Google Scholar
5. Y. Chen, J. Yang and W. Ribarsky, “Toward Effective Insight Management in Visual Analytics Systems”, Proc. IEEE Pacific Visualization Symp., pp. 49-56, 2009. Show Context Google Scholar
6. C. North, “Toward Measuring Visualization Insight”, IEEE Computer Graphics and Applications, vol. 26, no. 3, pp. 6-9, 2006. Show Context View Article Full Text: PDF (290KB) Google Scholar
7. J.S. Yi et al., “Understanding and Characterizing Insights: How Do People Gain Insights Using Information Visualization?”, Proc. ACM Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization, 2008. Show Context Access at ACM Google Scholar
8. M. Brehmer et al., “Pre-design Empiricism for Information Visualization: Scenarios Methods and Challenges”, Proc. 5th ACM Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization, pp. 147-151, 2014. Show Context Access at ACM Google Scholar
9. S.K. Card, J. Mackinlay and B. Shneiderman, Readings in Information Visualization: Using Vision to Think, Morgan Kaufmann, 1999. Show Context Google Scholar
10. P. Saraiya, C. North and C. Duca, “An Insight-Based Methodology for Evaluating Bioinformatics Visualizations”, IEEE Trans. Visualization and Computer Graphics, vol. 11, no. 4, pp. 443-456, 2005. Show Context View Article Full Text: PDF (3500KB) Google Scholar
11. H. Yang, Y. Li and M.X. Zhou, “Understand Users’ Comprehension and Preferences for Composing Information Visualizations”, ACM Trans. Computer-Human Interaction, vol. 21, no. 1, 2014. Show Context Access at ACM Google Scholar
12. I. Li, A. Dey and J. Forlizzi, “Understanding My Data Myself: Supporting Self-Reflection with Ubicomp Technologies”, Proc. 13th Int’l ACM Conf. Ubiquitous Computing, pp. 405-414, 2011. Show Context Access at ACM Google Scholar
13. J. Rooksby et al., “Personal Tracking as Lived Informatics”, Proc. SIGCHI Conf. Human Factors in Computing Systems, pp. 1163-1172, 2014. Show Context Access at ACM Google Scholar
14. R. Amar, J. Eagan and J. Stasko, “Low-Level Components of Analytic Activity in Information Visualization”, Proc. IEEE Symp. Information Visualization, pp. 111-117, 2005. Show Context Google Scholar
15. A.E. Kazdin, “Reactive Self-Monitoring: The Effects of Response Desirability Goal Setting and Feedback”, J. Consulting and Clinical Psychology, vol. 42, no. 5, pp. 704-716, 1974. Show Context CrossRef Google Scholar
16. J. Mackinlay, P. Hanrahan and C. Stolte, “Show Me: Automatic Presentation for Visual Analysis”, IEEE Trans. Visualization and Computer Graphics, vol. 13, no. 6, pp. 1137-1144, 2007. Show Context View Article Full Text: PDF (609KB) Google Scholar
17. B. Lee, R.H. Kazi and G. Smith, “SketchStory: Telling More Engaging Stories with Data through Freeform Sketching”, IEEE Trans. Visualization and Computer Graphics, vol. 19, no. 12, pp. 2416-2425, 2013. Show Context Google Scholar