Some critical and practical issues in Information Visualization
In this lesson we will work through various presentations of data and compare them to see if the rhetorical force of each visual format becomes clear, as well as examples of where a particular chart, graph, or diagram simply does not work. The effective use of different graphical forms is an art, and though it has no easy rules, it is governed by basic principles (as per the previous session). The chance to look at “best” and “worst” examples is also built into the exercises below, and this provides an opportunity to create a critical vocabulary for discussing why something is a poor visualization. From such descriptions, basic principles should arise and become clear, though one basic principle is that there are cases in which no standard treatment applies and the solution must be tailored to the problem and/or purpose for which the visualization is being design.
Take a simple data set (ages of everyone you know, put into a simple spread sheet) and display it in at least five different ManyEyes visualizations. Or, use one of their data sets and do the same thing. Which make sense? Which do not? Why? What does the exercise teach you about the rhetoric of information graphics?
- Charles Minard’s Chart (1869) (http://en.wikipedia.org/wiki/File:Minard.png)
Exercise: List the elements in the chart, how are they correlated?
- Pioneer Plaque (1972) (http://en.wikipedia.org/w/index.php?title=File:Pioneer_plaque.svg&page=1)
Exercise: What is the information being communicated? Suggest changes.
- Best and Worst; http://flowingdata.com/
Exercise: Name your own best/worst: when do the graphics overwhelm content?
3) Project related:
Using some aspect of your project, design an information visualization. Then think about how to use the different graphic variables (color, shape, size, orientation, value, texture, position) to designate a different feature of your data and/or your graphic.
- Jacques Bertin: seven graphic variables (http://www.infovis-wiki.net/index.php?title=Visual_Variables)
Exercise: Designate a role for each of these in your own visualization.
- Look at half a dozen examples on this site, Visual Complexity: (http://www.visualcomplexity.com/vc/)
What are the dimensions added here? What is the correlation between graphic expression and information?
- Are the aesthetics in these projects overwhelming the information? Or are they simply integrated into it? 10 Best Data Visualization of 2010(http://flowingdata.com/2010/12/14/10-best-data-visualization-projects-of-the-year-%E2%80%93-2010/)
5) Critical analysis:
Stanford Spatial History (http://www.stanford.edu/group/spatialhistory/cgi-bin/site/index.php)
Exercise: Analyze and critique, Mapping the Republic of Letters(http://www.stanford.edu/group/toolingup/rplviz/)
6) Advanced study:
Look at Edward Tufte’s first chapter in the Visual Display of Quantitative Information, and ask whether “form follows data.”
No data has an inherent visual form. Any data set can be expressed in any number of standard formats, but only some of these are appropriate for the features of the data. Certain common errors include mis-use of area, continuity, and other graphical qualities. The rhetorical force of visualization is often misleading. All visualizations are interpretations, not presentations of fact.
Many graphic features of visualizations are artifacts of the display, not of the data.
A visualization is an efficient way to show lots of information/data in succinct and legible manner. But it can also be “The reification of mis-information.”
Required readings for 5B
- William Turkel, Data Mining with Criminal Intent (http://criminalintent.org/getting-started/)
- Andrew Smith’s Commentary (http://andrewdsmith.wordpress.com/2011/08/21/the-promise-of-digital-humanities/)
Study questions for 5B:
- What is data mining?
- How does the interface to the Old Bailey change from the first to second versions?
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