Which Properties are Appropriate for Which Information Types?

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.

Recently Viewed Presentations

  • Warm Up 5/15 (#1)

    Warm Up 5/15 (#1)

    that was laundered through a Mexican bank and deposited in the account of Watergate burglar Bernard Barker. Later it was discovered that Former Attorney General John Mitchell, head of Nixon's "Committee to Re-Elect the President," (CREEP) controlled a . secret...
  • Chapter 6: The Primates - Houston Community College

    Chapter 6: The Primates - Houston Community College

    Dividing Primates . Strepsirhine (Strepsirhini): suborder of the order Primates that includes the prosimians, excluding the tarsier.. Haplorhine (Haplorhini): suborder of the order Primates that includes the anthropoids and the tarsier. Prosimian: member of the primate suborder prosimii that includes...
  • Fourth National Climate Assessment, Vol II Impacts, Risks,

    Fourth National Climate Assessment, Vol II Impacts, Risks,

    Key Message #3. 29. Ch. 29 | Reducing Risks Through Emissions Mitigation. Many climate change impacts and associated economic damages in the United States can be substantially reduced over the course of the 21st century through global-scale reductions in greenhouse...
  • Regulation of Animal Welfare - Purdue University

    Regulation of Animal Welfare - Purdue University

    The Silver Springs Monkey Case. ... for each animal, the motor and sensory nerves of one arm were severed). Dr. Taub was studying regeneration of severed nerves. Mr. Pacheco took a series of colored photographs of the condition of the...
  • DSS Chapter 1

    DSS Chapter 1

    Real-time location information = real-time insight Path Intelligence (pathintelligence.com) Footpath - movement patterns within a city or store How to use such movement information Application Case 7.2 Quiznos Targets Customers for Its Sandwiches Questions for Discussion How can location-based analytics...
  • ASE 2020 key messages to support development

    ASE 2020 key messages to support development

    These are, as stated, practicals, not investigations. Care needs to be taken that the practical is adapted so that it does allow ELC students to demonstrate achievement in all the Skill areas, particularly in Skill areas A, C and D....
  • Seeking Synchronicity: Evaluating Virtual Reference Transcripts Presented by

    Seeking Synchronicity: Evaluating Virtual Reference Transcripts Presented by

    Seeking Synchronicity: Evaluating Virtual Reference Services from User, Non-User, and Librarian Perspectives $1,103,572 project funded by: Institute of Museum and Library Services $684,996 grant Rutgers, The State University of New Jersey and OCLC Online Computer Library Center $405,076 in kind...
  • Could morphological knowledge improve literacy in dyslexic ...

    Could morphological knowledge improve literacy in dyslexic ...

    Could morphological knowledge improve literacy in dyslexic children? Professor Julia Carroll. ... Understands how words can combine to give new meanings. ... Could morphological knowledge improve literacy in dyslexic children?