Cathryn Ploehn
This resource gives a summary of information visualization in the following parts:
- An overview meant for researchers outside the visualization field,
- approaches to measurement and evaluation (or lack and need of such),
- and a proposed visualization task in the traffic domain
"Graphics is the visual means of resolving logical problems" - Jacques Bertin
##Contents
[Guiding Principles of Information Visualization](Guiding Principles of Information Visualization.md)
[Semiology of visualization](Semiology of visualization.md)
[Types of Visualizations](Types of Visualizations.md)
[Assessing information visualization](Assessing Information Visualization.md)
[Designing a new evaluation framework](Designing a new evaluation framework.md)
##What is visualization?
Visualization has become a loaded word.
xkcd
Chen et al. (2014) define visualization as:
"Visualization is a study of transformation from data to visual representations in order to facilitate effective and efficient cognitive processes in performing tasks involving data."
There are many types of visualizations, however, I'll focus on computer supported data visualization, which features data and computers.
Bertin (1981) distinguishes between graphics used for communication and graphics leveraged in processing data. The process of solving a problem involves distinct types of graphics, or visualizations. Knowledge visualizations communicate the answers discovered through information visualizations. Information visualizations are visual analytic tools for exploring and extracting patterns from abstract data.
We use knowledge visualization to communicate ideas:
xkcd
And we use (sometimes cryptic) information visualization to discover patterns in data:
spatial-analyst.net
In addition to focusing on computer supported data visualization, I will also focus on information visualization. (Although some concepts may also apply to knowledge visualization)
Card et al. (1999) refer to data visualization and information visualization as:
"the use of computer-supported, interactive, visual representations of abstract data to amplify cognition."
##Why information visualization is important
The display of data is a potentially powerful tool for data analysis.
Visualization -
- allows thorough exploration of the structure of data (Cleveland 1994). Many techniques of data analysis, which leverage data reduction - reduce the data to a handful of statistics.
- is a form of data analysis that can be leveraged to "convey the wealth of information that exists in data" (Cleveland 1994).
- allows for the encounter or surprises of the data, "yielding conclusions to questions not originally asked" (Cleveland 1994).
"The graph retains the information in the data" - W. Edwards Deming
Visualizations are powerful because it they are tools that can "strengthen our mental abilities." (Card et al. 1999, Bertin 1983, Norman 1993, Tversky 2001, Ware 2004)
Chen et al. (2014) also point out one of the most attractive, measureable features of visualization from a cost-benefit point of view:
Visualization saves time.
#Disclaimer
“The United States Department of Commerce (DOC) GitHub project code is provided on an ‘as is’ basis and the user assumes responsibility for its use. DOC has relinquished control of the information and no longer has responsibility to protect the integrity, confidentiality, or availability of the information. Any claims against the Department of Commerce stemming from the use of its GitHub project will be governed by all applicable Federal law.Any reference to specific commercial products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply their endorsement, recommendation or favoring by the Department of Commerce. The Department of Commerce seal and logo, or the seal and logo of a DOC bureau, shall not be used in any manner to imply endorsement of any commercial product or activity by DOC or the United States Government.”
Bertin, J. (1981). Graphics and graphic information processing. Walter de Gruyter.
Bertin, J. (1983). Semiology of graphics: diagrams, networks, maps.
Card, S. K., & Mackinlay, J. (1997, October). The structure of the information visualization design space. In Information Visualization, 1997. Proceedings., IEEE Symposium on (pp. 92-99). IEEE.
Card, S. K., Mackinlay, J. D., & Shneiderman, B. (1999). Readings in information visualization: using vision to think. Morgan Kaufmann.
Chen, M., Floridi, L., & Borgo, R. (2014). What is visualization really for?. In The Philosophy of Information Quality (pp. 75-93). Springer International Publishing.
Chen, C., & Yu, Y. (2000). Empirical studies of information visualization: a meta-analysis. International Journal of Human-Computer Studies, 53(5), 851-866.
Cleveland, W. S., & McGill, R. (1984). Graphical perception: Theory, experimentation, and application to the development of graphical methods. Journal of the American statistical association, 79(387), 531-554.
Mackinlay, J. (1986). Automating the design of graphical presentations of relational information. Acm Transactions On Graphics (Tog), 5(2), 110-141.
Norman, D. A. (1993). Things that make us smart: Defending human attributes in the age of the machine. Basic Books.
Tversky, B. (2001). Spatial schemas in depictions. In Spatial schemas and abstract thought (pp. 79-111).
Ware, C. (2004). Information visualization: perception for design. San Francisco, CA: Morgan Kaufmann.