Which Properties are Appropriate for Which Information Types?
Which Properties are Appropriate for Which Information Types? Accuracy Ranking of Quantitative Perceptual Tasks Estimated; only pairwise comparisons have been validated (Mackinlay 88 from Cleveland & McGill) Interpretations of Visual Properties
Some properties can be discriminated more accurately but dont have intrinsic meaning (Senay & Ingatious 97, Kosslyn, others) Density (Greyscale) Darker -> More
Size / Length / Area Larger -> More Position Leftmost -> first, Topmost -> first Hue
??? no intrinsic meaning Slope ??? no intrinsic meaning Ranking of Applicability of Properties for Different Data Types (Mackinlay 88, Not Empirically Verified)
QUANTITATIVE ORDINAL NOMINAL
Position Length Angle Slope Area Volume Density
Color Saturation Color Hue Position Density Color Saturation Color Hue
Texture Connection Containment Length Angle Position
Color Hue Texture Connection Containment Density Color Saturation Shape
Length Color Purposes Call attention to specific items Distinguish between classes of items Increases the number of dimensions for encoding Increase the appeal of the visualization
Using Color Proceed with caution Less is more Representing magnitude is tricky
Examples Red-orange-yellowyellow white Works for costs
Maybe because people are very experienced at reasoning shrewdly according to cost Green-light green-light brown-dark brown-greygrey white atlases Grayscale is unambiguous but has limited range
works for Visual Illusions People dont perceive length, area, angle, brightness they way they should. Some illusions have been reclassified as systematic perceptual errors
e.g., brightness contrasts (grey square on white background vs. on black background) partly due to increase in our understanding of the relevant parts of the visual system Nevertheless, the visual system does some
really unexpected things. Illusions of Linear Extent Mueller-Lyon (off by 25-30%) Horizontal-Vertical
Illusions of Area Delboeuf Illusion Height of 4-story building overestimated by approximately 25%
What are good guidelines for Infoviz? Use graphics appropriately Dont use images gratuitously Dont lie with graphics! Link to original data
Dont conflate area with other information E.g., use area in map to imply amount Match mental models About Edward Tufte Edward Rolf Tufte is an American statistician and professor emeritus of political science,
statistics, and computer science at Yale University. He is noted for his writings on information design and as a pioneer in the field of data visualization. Quotes
Beautiful Evidence is about the theory and practice of analytical design. The commonality between science and art is in trying to see profoundly - to develop strategies of seeing and showing. The leading edge in evidence presentation is in science; the leading edge in beauty is in high art.
Tufte Principles of Graphical Excellence Graphical excellence is the well-designed presentation of interesting data a matter of substance, of statistics, and of design consists of complex ideas communicated with clarity,
precision and efficiency is that which gives to the viewer the greates number of ideas in the shortest time with the least ink in the smallest space requires telling the truth about the data. Tuftes Notion of Data Ink Maximization
What is the main idea? draw viewers attention to the substance of the graphic the role of redundancy principles of editing and redesign Whats wrong with this? What is he really
getting at? Tufte Principle Maximize the data-ink ratio: data ink Data-ink ratio = -------------------------total ink used in graphic
Avoid chart junk Tufte Principles Use multifunctioning graphical elements Use small multiples Show mechanism, process, dynamics, and causality
High data density Number of items/area of graphic This is controversial White space thought to contribute to good visual design Tuftes book itself has lots of white space Tuftes Graphical Integrity
Some lapses intentional, some not Lie Factor = size of effect in graph size of effect in data Misleading uses of area Misleading uses of perspective Leaving out important context Lack of taste and aesthetics
From Tim Cravens LIS 504 course http://instruct.uwo.ca/fim-lis/504/504gra.htm#data-ink_ratio How to Exaggerate with Graphs from Tufte 83 Lie factor = 2.8
How to Exaggerate with Graphs from Tufte 83 Error: Shrinking along both dimensions
How to Display Data Badly Seeing is Believing? Not always http://www.webpagesthatsuck.com/worst-websites-of-2014.html
The Solution: The double y-axis graph The most powerful tool for misleading graphics ever devised. I gather, young man, that you wish to be a Member of
Parliament. The first lesson that you must learn is, when I call for statistics about the rate of infant mortality, what I want is proof that fewer babies died when I was Prime Minister than when anyone else was Prime Minister. That is a political statistic. Winston Churchill (1874-1965)
Of good graphs it may be said what Mark Van Doren observed about brilliant conversationalists: In their presence others speak well. A good graph is quiet and lets the data tell their story clearly and completely.
The four purposes of graphs 1. Exploration - The data contain a message and we would like to find out what it is. 2. Communication - We know something and we would like to tell others. 3. Calculation - Graphs can serve as visual algorithms (nomogaphs) that enable us to determine at-a-glance what might otherwise be tedious to calculate.
4. Decoration - Graphs are pretty and can be used to enliven what might otherwise be a dull presentation.
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