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Information Visualisation - Lecture 3

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Information Visualisation - Lecture 3

  1. 1. @wassx#ILV Informationsvisualisierungen Information Visualisation Information Visualisation Lecture 3 - Visualisation
  2. 2. #ILV Informationsvisualisierungen 2 A story…
  3. 3. #ILV Informationsvisualisierungen 3 Cognition Sitting in park, reading newspaper. Suddenly something appears in the corner of your eye. You raise the hand to block. Afterwards you recognise that a ball nearly hit your face.
  4. 4. #ILV Informationsvisualisierungen 4 Cognition Lesson learned #1 Vision is fast, but reason is slow.
  5. 5. #ILV Informationsvisualisierungen 5 Cognition Lesson learned #2 Your brain calculated estimated position of impact and prompt your arms to react.
  6. 6. #ILV Informationsvisualisierungen 6 Cognition Lesson learned #3 Seeing, perceiving and knowing are different phenomena.
  7. 7. #ILV Informationsvisualisierungen 7 Gestalt Laws
  8. 8. #ILV Informationsvisualisierungen 8 Gestalt Laws Attempt to understand pattern perception. Clear description of many basic perceptual phenomena. 1912 - Gestalt school of psychology
 (Max Wertheimer, Kurt Koffka and Wolfgang Köhler) Koffka WertheimerKöhler
  9. 9. #ILV Informationsvisualisierungen 9 Gestalt Laws
  10. 10. #ILV Informationsvisualisierungen 10 Gestalt Laws
  11. 11. #ILV Informationsvisualisierungen 11 Gestalt Laws Proximity Spatial proximity is a powerful organising principle. Things which are close together are perceived as a group. Additionally it has perceptual efficiency. Easier to pick information close to fovea, less time and effort will be spent in neural processing and eye. (-> cognitive load)
  12. 12. #ILV Informationsvisualisierungen 12 Gestalt Laws
  13. 13. #ILV Informationsvisualisierungen 13 Gestalt Laws
  14. 14. #ILV Informationsvisualisierungen 14 Gestalt Laws Similarity Shapes of individual pattern elements can also determine how they are grouped.
 Similar elements tend to be grouped together. Texture and color are separate channels Useful when design targets differentiation. Users can easily attend to either one pattern or the other.
  15. 15. #ILV Informationsvisualisierungen 15 Gestalt Laws
  16. 16. #ILV Informationsvisualisierungen 16 Gestalt Laws
  17. 17. #ILV Informationsvisualisierungen 17 Gestalt Laws Connectedness Steve Palmer and Irvin Rock argued that connectedness was overlooked by Gestalt psychologists. Palmer, Stephen; Neff, Jonathan; Beck, Diane (1997). "Grouping and Amodal Perception". In Rock, Irvin. Indirect perception. MIT Press/Bradford Books series in cognitive psychology. Connectedness can be more powerful than proximity, color, shape or size. Connecting with lines express relationships (node-link diagram)
  18. 18. #ILV Informationsvisualisierungen 18 Gestalt Laws
  19. 19. #ILV Informationsvisualisierungen 19 Gestalt Laws
  20. 20. #ILV Informationsvisualisierungen 20 Gestalt Laws Continuity Humans are more likely to construct visual entities out of visual elements that are smooth and continuous.
  21. 21. #ILV Informationsvisualisierungen 21 Gestalt Laws Continuity
  22. 22. #ILV Informationsvisualisierungen 22 Gestalt Laws
  23. 23. #ILV Informationsvisualisierungen 23 Gestalt Laws
  24. 24. #ILV Informationsvisualisierungen 24 Gestalt Laws Symmetry Symmetrically arranged pairs of lines are perceived more strongly as forming a visual whole than a pair of parallel lines. Makes pattern comparisons easier. Dakin and Herbert suggests that we are most sensitive to symmetrical patterns that are small in terms of visual angle ( <1 degree horizontally and <2 degrees vertically, and centered around the fovea) http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1689030/pdf/9608727.pdf
  25. 25. #ILV Informationsvisualisierungen 25 Gestalt Laws
  26. 26. #ILV Informationsvisualisierungen 26 Gestalt Laws Closure and Common Region Perceptual tendency to close contours that have gaps in them. (-> data ink ratio) Wherever a closed contour is seen, regions of space are divided into "inside" and "outside". Region enclosed by a contour becomes a common region. Common region much stronger than proximity.
  27. 27. #ILV Informationsvisualisierungen 27 Gestalt Laws Closure and Common Region
  28. 28. #ILV Informationsvisualisierungen 28 Gestalt Laws Figure and Ground https://www.pinterest.com/pin/562387072188816835/
  29. 29. #ILV Informationsvisualisierungen 29 Gestalt Laws Figure and Ground https://www.pinterest.com/pin/562387072188816835/ Brain decides what is the foreground (figure) in a scene. Decision is made on various cues: movement, color, size,… If not clear, figure competes with ground (cognitive load)
  30. 30. #ILV Informationsvisualisierungen 30 Gestalt Laws https://www.youtube.com/watch?v=nuH6dIcgaoU
  31. 31. #ILV Informationsvisualisierungen 31 Gestalt Laws Common Fate Mental grouping of entities which move in the same direction or have a common destination. Objects which share a common motion. https://www.windyty.com/?53.878,-27.993,4
  32. 32. #ILV Informationsvisualisierungen 32 Hands-on #3 Find a information graphic or visualisation and discuss in one paragraph the use of the Gestalt Principles. (Good example / bad example) ~15min
  33. 33. #ILV Informationsvisualisierungen 33 Visual Properties for Encoding
  34. 34. #ILV Informationsvisualisierungen 34 Visual Properties for Encoding Designing Data Visualizations, Noah Iliinsky & Julie Steele Choosing Appropriate Visual Encodings Different properties for different type of data. Key factors of a visual property are: 1. property is naturally ordered 2. how many distinct values reader can easily differentiate
  35. 35. #ILV Informationsvisualisierungen 35 Visual Properties for Encoding Designing Data Visualizations, Noah Iliinsky & Julie Steele Natural Ordering „Natural Order“ is determined by our visual system and „software“ in our brains by unintentionally assigning an order, or ranking to different values of that property. Independent of language, culture, convention,…
  36. 36. #ILV Informationsvisualisierungen 36 Visual Properties for Encoding Designing Data Visualizations, Noah Iliinsky & Julie Steele Natural Ordering quantitative differences ordinal differences
  37. 37. #ILV Informationsvisualisierungen 37 Visual Properties for Encoding Designing Data Visualizations, Noah Iliinsky & Julie Steele Natural Ordering How about COLOR?
  38. 38. #ILV Informationsvisualisierungen 38 Visual Properties for Encoding Designing Data Visualizations, Noah Iliinsky & Julie Steele Natural Ordering How about COLOR?
  39. 39. #ILV Informationsvisualisierungen 39 Visual Properties for Encoding Designing Data Visualizations, Noah Iliinsky & Julie Steele Natural Ordering No.
  40. 40. #ILV Informationsvisualisierungen 40 Visual Properties for Encoding Designing Data Visualizations, Noah Iliinsky & Julie Steele Natural Ordering Color (hue) is NOT naturally ordered. „Ordering“ based on social conventions about color and ordering by wavelength in the physical world. But no non-negotiable natural ordering built into our brain. 3 4vs.
  41. 41. #ILV Informationsvisualisierungen 41 Designing Data Visualizations, Noah Iliinsky & Julie Steele Natural Ordering Visual Properties for Encoding But luminance and saturation are naturally ordered.
  42. 42. #ILV Informationsvisualisierungen 42 Designing Data Visualizations, Noah Iliinsky & Julie Steele Distinct Values Visual Properties for Encoding Reader must be able to perceive, differentiate and remember distinct values. Big amount of values.
  43. 43. #ILV Informationsvisualisierungen 43 Visual Properties for Encoding Designing Data Visualizations, Noah Iliinsky & Julie Steele
  44. 44. #ILV Informationsvisualisierungen 44 https://www.behance.net/gallery/11685745/Datavisualisation-of-a-Game-of-Thrones
  45. 45. #ILV Informationsvisualisierungen 45 Visual Properties for Encoding Designing Data Visualizations, Noah Iliinsky & Julie Steele
  46. 46. #ILV Informationsvisualisierungen 46 Designing Data Visualizations, Noah Iliinsky & Julie Steele Redundant Encoding Visual Properties for Encoding If unused visual properties are left, consider using them for redundantly encode dimensions. Using more channels makes acquisition of information faster, easier and more accurate.
  47. 47. #ILV Informationsvisualisierungen 47 Visual Properties for Encoding Don’t forget…
  48. 48. #ILV Informationsvisualisierungen 48 Designing Data Visualizations, Noah Iliinsky & Julie Steele Compatibility with Reality Visual Properties for Encoding Align encodings with things and relationships known from reality. Compatibility Extra cues from physical world and cultural conventions.
  49. 49. #ILV Informationsvisualisierungen 49 Visual Properties for Encoding Designing Data Visualizations, Noah Iliinsky & Julie Steele
  50. 50. #ILV Informationsvisualisierungen 50 Designing Data Visualizations, Noah Iliinsky & Julie Steele
  51. 51. #ILV Informationsvisualisierungen 51 Visual Properties for Encoding Designing Data Visualizations, Noah Iliinsky & Julie Steele
  52. 52. #ILV Informationsvisualisierungen 52 Visual Properties for Encoding http://www.mymarketresearchmethods.com/wp-content/uploads/2013/01/visualization1.png
  53. 53. #ILV Informationsvisualisierungen 53 Visual Properties for Encoding Think for whom you are designing for. Keep in mind ~7% of males have some kind of color weakness. Check used colors with appropriate tools: Colorblind Vision Photoshop Online tools….
  54. 54. #ILV Informationsvisualisierungen 54 Visualising Patterns over Time
  55. 55. #ILV Informationsvisualisierungen 55 Visualising Patterns over Time
  56. 56. #ILV Informationsvisualisierungen 56 Visualising Patterns over Time http://projects.flowingdata.com/life-expectancy/
  57. 57. #ILV Informationsvisualisierungen 57 Visualising Patterns over Time http://skedasis.com/d3/slopegraph/
  58. 58. #ILV Informationsvisualisierungen 58 Visualising Proportions
  59. 59. #ILV Informationsvisualisierungen 59 Visualising Proportions
  60. 60. #ILV Informationsvisualisierungen 60 Visualising Proportions
  61. 61. #ILV Informationsvisualisierungen 61 Visualising Proportions
  62. 62. #ILV Informationsvisualisierungen 62 Visualising Proportions
  63. 63. #ILV Informationsvisualisierungen 63 Visualising Relations
  64. 64. #ILV Informationsvisualisierungen 64 Visualising Relations http://mbostock.github.io/d3/talk/20111116/iris-splom.html
  65. 65. #ILV Informationsvisualisierungen 65 Visualising Relations http://bl.ocks.org/mbostock/4063530
  66. 66. #ILV Informationsvisualisierungen 66 Visualising Relations http://bl.ocks.org/mbostock/4063550
  67. 67. #ILV Informationsvisualisierungen 67 Visualising Relations http://bl.ocks.org/mbostock/4063530 sankey
  68. 68. #ILV Informationsvisualisierungen 68 Spotting Differences
  69. 69. #ILV Informationsvisualisierungen 69 Spotting Differences http://bl.ocks.org/tjdecke/5558084
  70. 70. #ILV Informationsvisualisierungen 70 Spotting Differences http://bl.ocks.org/tjdecke/5558084
  71. 71. #ILV Informationsvisualisierungen 71 Spotting Differences Chernoff faces
  72. 72. #ILV Informationsvisualisierungen 72 Spotting Differences Chernoff faces
  73. 73. #ILV Informationsvisualisierungen 73 Visualising Spatial Relationships
  74. 74. #ILV Informationsvisualisierungen 74 Visualising Spatial Relationships Airport data
  75. 75. #ILV Informationsvisualisierungen 75 Visualising Spatial Relationships http://ssz.fr/parite/
  76. 76. #ILV Informationsvisualisierungen 76 Visualising Spatial Relationships http://avtanski.net/projects/gps/
  77. 77. #ILV Informationsvisualisierungen 77
  78. 78. #ILV Informationsvisualisierungen 78
  79. 79. #ILV Informationsvisualisierungen 79 Checklist
  80. 80. #ILV Informationsvisualisierungen 80 Checklist Determine Your Goals and Supporting Data • What information need are you attempting to satisfy with this visualization? • What values or data dimensions are relevant in this context? • Which of these dimensions matter; matter most; and matter least? • What are the key relationships that need to be communicated? • What properties or values may make some individual data points more interesting than the rest? • What actions might be taken once the reader’s information need is satisfied, and what values will justify that action? Designing Data Visualizations, Noah Iliinsky & Julie Steele, O'Reilly 2011
  81. 81. #ILV Informationsvisualisierungen 81 Checklist Consider Your Reader • What information does the reader need to be successful? • How much detail does the reader need? • How long does the reader have to make any learned information effective? • What learned or cultural assumptions does the reader have that may affect your design choices? Designing Data Visualizations, Noah Iliinsky & Julie Steele, O'Reilly 2011
  82. 82. #ILV Informationsvisualisierungen 82 Checklist Select Axes, Layout, and Placement • Can you encode your most important data dimension or relationship with position? • Is there a secondary grouping, dimension, or relationship that can be represented spatially? What if you rearrange or invert groupings? • Does your direction make sense? Where does the data begin and end? Where should the reader start reading? Which way to the relationships flow? • Does the placement of your entities reflect their relationships to each other? • Does the placement of your entities reflect their relationship to reality? Designing Data Visualizations, Noah Iliinsky & Julie Steele, O'Reilly 2011
  83. 83. #ILV Informationsvisualisierungen 83 Checklist Evaluate Your Encoding Entities • Are you using conventional encodings and formats? If not, are you sure you have something better? • Are you using color to represent quantity? Stop it. Use size or placement instead. • Are your shapes, colors, icons, and text evocative of the properties that exist and that you want to communicate? • Are you using the same visual encoding for more than one data dimension? Try to pick another one. • Are you using extra visual properties to redundantly encode your data? Good job! Designing Data Visualizations, Noah Iliinsky & Julie Steele, O'Reilly 2011
  84. 84. #ILV Informationsvisualisierungen 84 Checklist Reveal the Data’s Relationships • Are the most important relationships revealed? • Do the relationships need to be called out with links or labels? Or a specific flag? • Are all the displayed relationships actually relevant and useful? • Are you redundantly encoding your links? Designing Data Visualizations, Noah Iliinsky & Julie Steele, O'Reilly 2011
  85. 85. #ILV Informationsvisualisierungen 85 Checklist Choose Titles, Tags, and Labels • Is the reader from within your industry or outside of it? What about other readers outside of the core audience group? Consider how this will affect your vocabulary choices. • Is it worth using an industry term for the sake of precision (knowing that the reader may have to look it up), or would a lay term work just as well? • Will the reader be able to decipher any unknown terms from context, or will a vocabulary gap obscure the meaning of all or part of the information presented? • Is everything important labeled? Are all of your labels necessary? Is your key or legend necessary? Is it ordered in a useful way? Designing Data Visualizations, Noah Iliinsky & Julie Steele, O'Reilly 2011
  86. 86. #ILV Informationsvisualisierungen 86 Checklist Analyze Patterns and Consistency • Have you been consistent in membership, ordering, placement, and other encodings? • Things that are the same should look the same. Is that so? • Things that are different should look different. Is that so? Designing Data Visualizations, Noah Iliinsky & Julie Steele, O'Reilly 2011

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