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04 data viz concepts


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04 data viz concepts

  1. 1. Data Visualization ConceptsPrepared by:Paul Kahn – Experience Design DirectorFebruary, 2013Media Lab, Aalto UniversityHelsinki, Finland
  2. 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. 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. 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. 5. Seven Visual Variables To Represent Data 5
  6. 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. 7. 7Variables of the Image (Beniot Martin)
  8. 8. 8Differential Variables (4-5 )• Grain/Pattern: Variation of value within glyph• Color: hue of glyph content
  9. 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. 10. 10Les variables visuelles (Beniot Martin)
  11. 11. 11TGV NetworkNetwork map 2011
  12. 12. 12TGV Network• X/Y Position• Size: Z value of quantity (area)• Value: Z value of content (fill)• Grain/Pattern• Color• Orientation• Shape
  13. 13. 13TGV Network• X/Y Position• Size: Z value of quantity (area)• Value: Z value of content (fill)• Grain/Pattern• Color• Orientation• Shape
  14. 14. 14TGV NetworkTGV Change of servicespeed to MarseilleBEFORE
  15. 15. 15TGV NetworkTGV Change of servicespeed to MarseilleAFTER
  16. 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. 17. 17Online resourcesBrewer, C. A. 1999. Color Use Guidelines for DataRepresentation, Proceedings of the Section onStatistical Graphics, American StatisticalAssociation more excuses: a list of references to learn howto use color
  18. 18. 18Dashboard example
  19. 19. 19Dashboard example
  20. 20. 20CogSci Theory (Dan Berlin)Pre-attentive Visual Variables (1-4) From Designing Interfaces by Jenifer Tidwell
  21. 21. 21Pre-attentive Visual Variables (5-8)
  22. 22. 22Don’t make me thinkImmediate Visual Scan Repeated Visual ScanAn interaction is intuitivewhen the user makes the least effort to grasp thedifference.
  23. 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. 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. 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. 26. Example: Periodic Table of ElementsDmitri Mendeleev’s original table (1869)
  27. 27. Periodic Table as a metaphor
  28. 28. Displaying Quantity in LocationWilliam Playfair (1759-1823): space as a metaphor for quantity
  29. 29. 31Charles Joseph Minard (1781-1870)Thickness of line(also known as aSankey Diagram)
  30. 30. Otto Neurath (1882-1945), Gerd Arntz (1900-1988) — Isotype: Repeated unit as an expression for quantity
  31. 31. Otto Neurath, Modern Man in the Making (1939)
  32. 32. Maps & Diagrams | September 2011 | 35
  33. 33. US Population density (2000), Read Agnew & Don Moyers,UNDERSTANDING USA