04 data viz concepts

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  • 1. Data Visualization ConceptsPrepared by:Paul Kahn – Experience Design DirectorFebruary, 2013Media Lab, Aalto UniversityHelsinki, Finland
  • 2. Gregory Bateson (1904-1980)British anthropologist, social scientist, linguist, visualanthropologist, semiotician and cyberneticist whose work intersectedthat of many other fieldsMajor books:Steps To An Ecology of the Mind, 1972Mind and Nature: A Necessary Unity, 1979
  • 3. Information and MindAll information is communicated as differencesThe mind operates with hierarchies and networks to create gestalten.Hierarchies are nested containersNetworks are links connecting discrete nodesInformation architecture is the re/shaping of information/differences into hierarchies and networks we search for and visualize the patterns that connectThe pattern that connects is the pathways for accessing differences
  • 4. Jacques Bertin (1918-2010)Visual Variables for Quantitative Information“Matrix theory of graphics,” Information Design Journal, Vol. 10, No. 1. (2002)Semiology of graphics: Diagrams, Networks, Maps (Univ of Wisconsin, 1983; ESRI, 2010)originally published as Sémiologie graphique (1967)
  • 5. Seven Visual Variables To Represent Data 5
  • 6. 6Variables of the Image (1-3)• X/Y Position• Size: Z value of quantity (area) superimposed on position• Value: Z value of content (fill) superimposed on position
  • 7. 7Variables of the Image (Beniot Martin)
  • 8. 8Differential Variables (4-5 )• Grain/Pattern: Variation of value within glyph• Color: hue of glyph content
  • 9. 9Differential Variables (6-7 )• Orientation: relative position in relation to XY grid• Shape: abstract shapes distinguished by outline: dots, squares, triangles, diamonds, metaphors
  • 10. 10Les variables visuelles (Beniot Martin)
  • 11. 11TGV NetworkNetwork map 2011
  • 12. 12TGV Network• X/Y Position• Size: Z value of quantity (area)• Value: Z value of content (fill)• Grain/Pattern• Color• Orientation• Shape
  • 13. 13TGV Network• X/Y Position• Size: Z value of quantity (area)• Value: Z value of content (fill)• Grain/Pattern• Color• Orientation• Shape
  • 14. 14TGV NetworkTGV Change of servicespeed to MarseilleBEFORE
  • 15. 15TGV NetworkTGV Change of servicespeed to MarseilleAFTER
  • 16. 16Color Use Guidelines for Data Representation Brewer, C. A. 1999. Color Use Guidelines for Data Representation, Proceedings of the Section on Statistical Graphics, American Statistical Association
  • 17. 17Online resourcesBrewer, C. A. 1999. Color Use Guidelines for DataRepresentation, Proceedings of the Section onStatistical Graphics, American StatisticalAssociationhttp://www.personal.psu.edu/cab38/ColorSch/ASApaper.htmlNo more excuses: a list of references to learn howto use colorhttp://diuf.unifr.ch/people/bertinie/visuale/2009/05/infovis_color_theory_in_few_li.html
  • 18. 18Dashboard example
  • 19. 19Dashboard example
  • 20. 20CogSci Theory (Dan Berlin)Pre-attentive Visual Variables (1-4) From Designing Interfaces by Jenifer Tidwell
  • 21. 21Pre-attentive Visual Variables (5-8)
  • 22. 22Don’t make me thinkImmediate Visual Scan Repeated Visual ScanAn interaction is intuitivewhen the user makes the least effort to grasp thedifference.
  • 23. 23Steps of Visual Cognition Preattentive Perception Cognition ProcessingPerception • All based on changes in contrast: hue, brightness, and color palette • We detect differences, physiologically and psychologicallyPre-attentive Processing • Processed in under 250 milliseconds (Healey, Booth, and Enns, 1995) • Parallel (bottom-up) processingCognition • Serial (top-down) processing
  • 24. 24 Elementary Perceptual TasksWe are good at some tasks,but not others• Good at: position, length, direction• Bad at: area (of a circle), volume, saturationThis is why you will seeline or bar graphs toconvey data• You will never (well, shouldn’t) see a graph that uses color saturation to convey data (i.e. using different shades of orange)
  • 25. 25Preattentive ProcessingSecond step of visual perception “The perception of a pattern can often • Sits between perception and cognition be the basis of a new insight.” • Processed in under 250 milliseconds - Colin Ware, Information Visualization • Understanding without training or cognition • Serial vs. parallel processing • Forms objects in the mind’s eyePreattentive variables • Proximity, similarity, connectedness, continuity, symmetry, closure, relative size, figure and ground, intensity, curvature, line length, color, orientation, brightness, and direction of movement. • Overlapping variables • Many theories as to how we deal with these – Feature Integration Theory, for one (2 variables at most)Variable hierarchy
  • 26. Example: Periodic Table of ElementsDmitri Mendeleev’s original table (1869)
  • 27. Periodic Table as a metaphor
  • 28. Displaying Quantity in LocationWilliam Playfair (1759-1823): space as a metaphor for quantity
  • 29. 31Charles Joseph Minard (1781-1870)Thickness of line(also known as aSankey Diagram)
  • 30. Otto Neurath (1882-1945), Gerd Arntz (1900-1988) — Isotype: Repeated unit as an expression for quantity
  • 31. Otto Neurath, Modern Man in the Making (1939)
  • 32. Maps & Diagrams | September 2011 | 35
  • 33. US Population density (2000), Read Agnew & Don Moyers,UNDERSTANDING USA