Critical Practice I: InfovisLuca - Campus Sint-Lukas Brussela/prof. Andrew Vande MoereDepartment of Architecture, Urbanism...
Hackers - United Artists - 1996
Tron - The Electronic Gladiator - 1982
Johnny Mnemonic - Tristar Pictures -1995
Cyber Swap Worlds - 1997
City of News - MIT Media Lab - 1997
VR/Search - Andrew Vande Moere - 1998
VR Data Visualization - ETH-Zurich
Information Visualization for Immersive VR - Andrew Vande Moere - 2004http://www.youtube.com/watch?v=AZmcrVplqDUVR Data Vi...
Stock Market Swarm - Andrew Vande Moere - 2004http://www.youtube.com/watch?v=LjUZ6vcTc1Q
Stock Market Swarm - Andrew Vande Moere - 2004http://www.youtube.com/watch?v=LjUZ6vcTc1Q
University Finances Visualization - Andrew Vande Moere - 2003
University Finances Visualization - Andrew Vande Moere - 2003http://www.youtube.com/watch?v=duxjQKgYtNY
Information Aesthetics - “Where Form Follows Data” - http://infosthetics.com
[Proposed] Time Plan 29 october 2012- Data + Perception + Data Mapping- Bad/Good Infographic Guidelines5 november 2012- In...
http://vimeo.com/34182381
Six Degrees of Mohamed Attahttp://business2.com/articles/mag/0,1640,35253,FF.html
US Terrorism Response Org Charthttp://www.cns.miis.edu/research/cbw/domestic.htm#wmdchart
Space Shuttle Launch. O-ring damage data. launch or not launch?. risk of human lives versus loosing reputation. ambient te...
Space Shuttle Launch - January 28, 1986
1854 - Epidemiological data chart
1854 - Epidemiological data chart
Choice of “Visual” / “Data”. anything can be ‘translated’ in anything. can be ineffective (wrong answers). can be inefficie...
How to Design Visualization?. 1. understanding properties of the image. 2. understanding properties of the data. 3. unders...
Visual Perception. 1. understanding properties of the image
Human perception governed by general principles?
Shark or submarine?http://en.wikipedia.org/wiki/File:SharkOrSubmarine4024617900.jpg
What to look for in data visualization...
Pre-Attentive Features. time taken to make a decision is constant. and is less than 200-250ms (< eye movement). independen...
‘Pop-out’ Features. form: line orientation, length, width, visual marks,.... color: hue, intensity, .... motion: flicker, d...
orienta(on                                                             size                             length,	  width   ...
Viewer can rapidly & accurately determine                 whether the target (red circle) is present or absent:           ...
Viewer can rapidly & accurately determine                  whether the target (red circle) is present or absent           ...
Viewer cannot rapidly & accurately determine whether target is present or absent when target has combined two or more feat...
Hue-on-form feature hierarchy: (a) a horizontal hue boundary is pre-  attentive identified when form is held constant; (b) ...
Hue-on-form feature hierarchy: (c) a vertical form boundary is preattentivelyidentified when hue is held constant; (d) hori...
Viewer cannot rapidly & accurately determine border by a conjunction of  features (red circles & blue squares on the left,...
Target has a unique feature with respect to distractors (i.e. open sides).                  The group can be detected pre-...
Target does not have a unique feature with respect to distractors.                 The group cannot be detected pre-attent...
A sloped line among vertical lines is pre-attentive.                   A sloped line among other sloped ones is not.Featur...
1281768756138976546984506985604982826762  9809858458224509856458945098450980943585  90910302099059595957725646750506789045...
1281768756138976546984506985604982826762  9809858458224509856458945098450980943585  90910302099059595957725646750506789045...
  ROOD	   GROEN	              BLAUW	             GEEL        	   ROZE	  	   ORANJE	         BLAUW	  	         GROEN       ...
Gestalt LawsHow can these be applied in information visualization?
Figure and Ground
Figure and Ground
Gestalt Laws: Law of Simplicityhttp://machineslikeus.com/the-constructive-aspect-of-visual-perception
Law of Simplicityhttp://www.excelcharts.com/blog/data-visualization-excel-users/gestalt-laws/
Law of ProximityWe group together things that seem near each other, and assume they are similar.
Law of SimilarityWe group together things that share the same color, shape, direction, ....
Law of SimilaritySuch as the choice of colors: richer color choice, forcing the orange parts to be together
Law of ConnectednessWe group things together that are visually connected. Stronger than similarity.
Law of ContinuityWe tend to connect things that are arranged in a smooth way, even when a visual ‘gap’.
Law of ClosureWe group things that are enclosed in visual shapes.
Law of SymmetryWe assume the horizontal / vertical part is identical. Good for before/after comparisons...
•pattern	  recognition •we	  are	  constantly	  grouping	  objects	  based	  on	  color,	    shape,	  direction,	  proximi...
Context influences visual perception!
71Context influences visual perception!
Context influences visual perception!
Data. 2. understanding properties of data
“Concrete” Data. carries spatial layout. position, color, visual characteristics. represented by graphical reproduction
“Abstract” Data. data without natural representation. requires metaphor to be perceived. data is “mapped” in visual form
Information Visualization?
Information Visualization (Scientific) VisualizationExamples                      Examplesstock market, friend          clo...
Kinds of data...S Stevens “On the theory of scales and measurements” (1946)
Quantitative Data. numerical, scalar values, arithmetic operations,...   . e.g. 4, 14, 5445453, 2, 1.00342, 3, ...        ...
Ordinal Data. larger / smaller than...   . e.g. Monday, Tuesday, Wednesday, ...                                           ...
Categorical / Hierarchical Data. can be categorized   . e.g. alphabetically, thematically, functionality   . e.g. desktop ...
Network / Relational Data. one-to-many, many-to-one relationships  . often these are ‘weighted’  . e.g. social networks, p...
Nominal / Unstructured Data. no order, no units, only “equal or different”  . e.g. Australia, Belgium, Mexico             ...
Temporal / Dynamic Data. time dependent   . related to progress of time, history, ...   . e.g. stock market, news stories,...
§ attributes § dimensions § variables § columns§ values § quantitative § ordinal § categorical § nominal§ items ...
§ dimensionality § # of attributes§ scale / size § # of items§ value range § bits/value, min/max, etc.§ time depend...
“The current complexity of data is  staggering, and our ability to collect  data is increasing at a faster rate that  our ...
How is data ‘complex’?. size: number of records. dimensionality: number of attributes. time-dependency: data changes over ...
Data is more complex now?. complexity of human society always increasing. quantity always increasing, never decreasing!. s...
An Inconvenient Truth - Al Gore
1. Social relevance - An Inconvenient Truth - 2006
An Inconvenient Truth - Al Gore
Complexity of data: The ‘real’ data behind “An Inconvenient Truth - Al Gore”
Data Mapping. 3. understanding how to map data to an image
Why mapping necessary?
because the data is abstract...                   “the challenge is to invent                          new metaphors for  ...
Data + data mapping = visual representation that can be (relatively fairly) interpreted
data                                 insight    10010110                   knowledge                                 trans...
data               §data   mapping requirements?                               § computable   10010110                  ...
Mon                                   Sun          Mon                               Sun15    17    19    15    22    10  ...
15   17   19   15   22   10   15   15   17   19   15   18   10   15   15   17   19   15   11   10   15                 15 ...
Example Movie Dataset: How to approach the data mapping?
1 items                                        data                                               ê                      ...
1 data items                                                ê                                         visual objects     ...
objects                              point, line, area, volume, ...                           properties                  ...
Example scatterplot of movie dataset Year → X Length → Y Popularity → size Subject → color Award? → shape
Data Mapping Limitations. data scale: one object for each tuple?. data dimensionality: visual cue for each attribute?. val...
(Cleveland and McGill)Guideline: Assign most important data pattern the most perceptual accurate visual cue.
Visual objects versus visual attributes.http://understandinggraphics.com/visualizations/information-display-tips/
What chart type to choose?http://extremepresentation.typepad.com/blog/2006/09/choosing_a_good.html
Intuitive data mapping....Less intuitive data mapping....
Context influences color perception!
http://www.nationalgeographic.nl/community/foto/bekijken/slow-motion-2Motion as Visual Cue. pre-attentive feature, can be ...
"An Experimental Study of Apparent Behaviour" (1944)Fritz Heider & Marianne Simmel
Simple motion, complex interpretation.http://www.biomotionlab.ca/Demos/BMLwalker.html
Motion properties
“‘Graphical Excellence’ is that which    gives to the viewer a great number of    ideas in the shortest time, with the    ...
French Invasion of Russia (Minard, +-1864)Napoleon Retreat (Minard, +-1864)
temperaturetimetemp[day]
longitudelatitudearmy[size, day]army[position, day]
1. Show comparisons, contrasts, differences2. Show causality, mechanism, explanation,systematic structure3. Show multivari...
1. Show comparisons, contrasts,differences
2. Show causality, mechanism, explanation, systematic structureFrench Invasion of Russia (Minard, +-1864)Napoleon Retreat ...
3. Show multivariate data; that is, show more than 1 or 2 variablesFrench Invasion of Russia (Minard, +-1864)Napoleon Retr...
4. Completely integrate words, numbers, images, diagramsFrench Invasion of Russia (Minard, +-1864)Napoleon Retreat (Minard...
5.Thoroughly describe the evidence: title, authors and sponsors, data sources, add measurement scales, highlight relevant ...
6.Analytical presentations ultimately stand or fall depending on the quality, relevance and integrity of their contentFren...
Telling a narrative with a “simple” design, though still “complex” data
Chart Components
1. Lie Factor2. Data Ink Ratio3. Data Density4. Chart Junk5. ExpressivenessPrinciples for the Analysis and Presentation of...
Lie FactorExaggeration of differences between values
Lie FactorExaggeration of differences between values
1. Lie FactorQuantifying the exaggeration of differences between values
1. Lie FactorExaggeration of differences between values
1. Lie FactorHow many times can 1978 fit into 1958?
2. Data-Ink Ratio2. Data-Ink RatioUnnecessary & distracting patterns - graphics emphasizing style over information
2. Data-Ink Ratio2. Data-Ink RatioUnnecessary & distracting patterns - graphics emphasizing style over information
Five Laws of “Data-Ink”• Above all else show (only) the data• Maximize the data-ink ratio• Erase non-data ink• Erase redun...
2. Data-Ink RatioUnnecessary & distracting gridlines - graphics emphasizing style over information
2. Data-Ink RatioUnnecessary & distracting patterns - graphics emphasizing style over information
3. Data Density“A pixel is a terrible thing to waste” Ben Shneiderman
3. Data Density“A pixel is a terrible thing to waste” Ben Shneiderman
4. Chart JunkQuestion: Is the data still visible without the graphics?How much can you take away before it becomes illegib...
4. Chart JunkIs the data still visible without the graphics?
4. Chart JunkIs the data still visible without the graphics?
4. Chart JunkIs the data still visible without the graphics?
4. Chart junkAvoid non-data ink (fonts, lines, aesthetics)                                                Growing wealth a...
4. Chart junkAvoid non-data ink (fonts, lines, aesthetics)
4. Chart junkAvoid non-data ink (fonts, lines, aesthetics)
5. ExpressivenessEncode the data - Encode only the data
5. ExpressivenessEncode the data - Encode only the data
5. ExpressivenessUse line charts for time-series or continuous data, never for categorical data.
5. ExpressivenessEncode the data - Encode only the data
5. ExpressivenessEncode the data - Encode only the data (and never use 3D on a 2D medium).
5. ExpressivenessEncode the data - Encode only the data
5. ExpressivenessEncode the data - Encode only the data
5. ExpressivenessWrite out explanations on the graphic itself.
5. ExpressivenessWrite out explanations on the graphic itself.
5. ExpressivenessAdd labels next to graph (then no legend required!)
5. ExpressivenessScaling: lines should cover 2/3 of data area.... (contentious issue!)
5. ExpressivenessColors: avoid high contrast
5. ExpressivenessColors: avoid high contrast
5. ExpressivenessColors: choose harmonious colors
5. ExpressivenessColors: for related set of data attributes, use similar colors.
5. ExpressivenessColors: use color for highlighting. Note difference if data attributes are related or not!
5. ExpressivenessOrientation text horizontally. Order by size, not by alphabet.
5. ExpressivenessComparing vertical axesjunkcharts.typepad.com only when identical...
5. ExpressivenessComparing vertical axesjunkcharts.typepad.com only when identical, or at least similar rate (e.g. 2x base...
5. ExpressivenessAvoid size as quantitativejunkcharts.typepad.com value. If so, map values as surface area (and never as r...
5. ExpressivenessAvoid size as quantitativejunkcharts.typepad.com value. If so, map values as surface area (and never as r...
5. ExpressivenessMaps only useful for spatial distribution. They do not take into account population densityjunkcharts.typ...
5. ExpressivenessAvoid donut charts...
junkcharts.typepad.com
China, Egypt, Mexico, South Africa, Philippines, India - and for different periods.
Homework- Find a recent,“Belgian” infographic- Analyze: dataset, data mapping, ...- Critique (good/bad) design decisions- ...
Thank you! Questions?------.----------@asro.kuleuven.be /// http://infosthetics.com /// @infosthetics
Introduction to Information Visualization (Part 1)
Introduction to Information Visualization (Part 1)
Introduction to Information Visualization (Part 1)
Introduction to Information Visualization (Part 1)
Introduction to Information Visualization (Part 1)
Introduction to Information Visualization (Part 1)
Introduction to Information Visualization (Part 1)
Introduction to Information Visualization (Part 1)
Introduction to Information Visualization (Part 1)
Introduction to Information Visualization (Part 1)
Introduction to Information Visualization (Part 1)
Introduction to Information Visualization (Part 1)
Introduction to Information Visualization (Part 1)
Introduction to Information Visualization (Part 1)
Introduction to Information Visualization (Part 1)
Introduction to Information Visualization (Part 1)
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Introduction to Information Visualization (Part 1)

  1. 1. Critical Practice I: InfovisLuca - Campus Sint-Lukas Brussela/prof. Andrew Vande MoereDepartment of Architecture, Urbanism & Planning - ASRO - KU Leuven------.----------@asro.kuleuven.be - http://infosthetics.com - @infosthetics
  2. 2. Hackers - United Artists - 1996
  3. 3. Tron - The Electronic Gladiator - 1982
  4. 4. Johnny Mnemonic - Tristar Pictures -1995
  5. 5. Cyber Swap Worlds - 1997
  6. 6. City of News - MIT Media Lab - 1997
  7. 7. VR/Search - Andrew Vande Moere - 1998
  8. 8. VR Data Visualization - ETH-Zurich
  9. 9. Information Visualization for Immersive VR - Andrew Vande Moere - 2004http://www.youtube.com/watch?v=AZmcrVplqDUVR Data Visualization
  10. 10. Stock Market Swarm - Andrew Vande Moere - 2004http://www.youtube.com/watch?v=LjUZ6vcTc1Q
  11. 11. Stock Market Swarm - Andrew Vande Moere - 2004http://www.youtube.com/watch?v=LjUZ6vcTc1Q
  12. 12. University Finances Visualization - Andrew Vande Moere - 2003
  13. 13. University Finances Visualization - Andrew Vande Moere - 2003http://www.youtube.com/watch?v=duxjQKgYtNY
  14. 14. Information Aesthetics - “Where Form Follows Data” - http://infosthetics.com
  15. 15. [Proposed] Time Plan 29 october 2012- Data + Perception + Data Mapping- Bad/Good Infographic Guidelines5 november 2012- Infovis, Storytelling,Research Results, ...- Compelling Dataviz Examples
  16. 16. http://vimeo.com/34182381
  17. 17. Six Degrees of Mohamed Attahttp://business2.com/articles/mag/0,1640,35253,FF.html
  18. 18. US Terrorism Response Org Charthttp://www.cns.miis.edu/research/cbw/domestic.htm#wmdchart
  19. 19. Space Shuttle Launch. O-ring damage data. launch or not launch?. risk of human lives versus loosing reputation. ambient temperature at launch: 25-30 degrees F
  20. 20. Space Shuttle Launch - January 28, 1986
  21. 21. 1854 - Epidemiological data chart
  22. 22. 1854 - Epidemiological data chart
  23. 23. Choice of “Visual” / “Data”. anything can be ‘translated’ in anything. can be ineffective (wrong answers). can be inefficient (takes too much effort). can be disengaging (no users, giving up, ...)
  24. 24. How to Design Visualization?. 1. understanding properties of the image. 2. understanding properties of the data. 3. understanding how to map data to an image
  25. 25. Visual Perception. 1. understanding properties of the image
  26. 26. Human perception governed by general principles?
  27. 27. Shark or submarine?http://en.wikipedia.org/wiki/File:SharkOrSubmarine4024617900.jpg
  28. 28. What to look for in data visualization...
  29. 29. Pre-Attentive Features. time taken to make a decision is constant. and is less than 200-250ms (< eye movement). independent of number of added detractors. primitive features, low-level visual processing. salience depends on: ‘strength’, and ‘context’
  30. 30. ‘Pop-out’ Features. form: line orientation, length, width, visual marks,.... color: hue, intensity, .... motion: flicker, direction of motion, .... spatial position: depth, convex/concave shape,...
  31. 31. orienta(on size length,  width closure curvature density,  contrast number,  es(ma(on colour  (hue)Some already known pre-attentive visual features
  32. 32. Viewer can rapidly & accurately determine whether the target (red circle) is present or absent: difference detected in colorPre-Attentive Processing - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
  33. 33. Viewer can rapidly & accurately determine whether the target (red circle) is present or absent difference detected in form (curvature)Pre-Attentive Processing - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
  34. 34. Viewer cannot rapidly & accurately determine whether target is present or absent when target has combined two or more features, also present in the distracters. Viewer must search sequentiallyPre-Attentive Processing - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
  35. 35. Hue-on-form feature hierarchy: (a) a horizontal hue boundary is pre- attentive identified when form is held constant; (b) a vertical hue boundary is pre-attentively identified when form varies randomly in the backgroundFeature Hierarchy - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
  36. 36. Hue-on-form feature hierarchy: (c) a vertical form boundary is preattentivelyidentified when hue is held constant; (d) horizontal form boundary cannot be pre- attentively identified when hue varies randomlyFeature Hierarchy - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
  37. 37. Viewer cannot rapidly & accurately determine border by a conjunction of features (red circles & blue squares on the left, blue circles and red squares on the right)Feature Hierarchy - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
  38. 38. Target has a unique feature with respect to distractors (i.e. open sides). The group can be detected pre-attentively.Feature Hierarchy - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
  39. 39. Target does not have a unique feature with respect to distractors. The group cannot be detected pre-attentively.Feature Hierarchy - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
  40. 40. A sloped line among vertical lines is pre-attentive. A sloped line among other sloped ones is not.Feature Hierarchy - http://www.csc.ncsu.edu/faculty/healey/PP/index.html
  41. 41. 1281768756138976546984506985604982826762 9809858458224509856458945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686Text pre-attentive?
  42. 42. 1281768756138976546984506985604982826762 9809858458224509856458945098450980943585 9091030209905959595772564675050678904567 8845789809821677654876364908560912949686Text pre-attentive?
  43. 43.   ROOD   GROEN   BLAUW   GEEL   ROZE     ORANJE   BLAUW     GROEN    BLAUW   BRUIN     GROEN     GEEL   ORANGE    BRUIN     BLAUW   BRUIN   ROOD     BLAUW   GEEL       GROEN   ROZE     GEEL     GROEN     BLAUWText pre-attentive?
  44. 44. Gestalt LawsHow can these be applied in information visualization?
  45. 45. Figure and Ground
  46. 46. Figure and Ground
  47. 47. Gestalt Laws: Law of Simplicityhttp://machineslikeus.com/the-constructive-aspect-of-visual-perception
  48. 48. Law of Simplicityhttp://www.excelcharts.com/blog/data-visualization-excel-users/gestalt-laws/
  49. 49. Law of ProximityWe group together things that seem near each other, and assume they are similar.
  50. 50. Law of SimilarityWe group together things that share the same color, shape, direction, ....
  51. 51. Law of SimilaritySuch as the choice of colors: richer color choice, forcing the orange parts to be together
  52. 52. Law of ConnectednessWe group things together that are visually connected. Stronger than similarity.
  53. 53. Law of ContinuityWe tend to connect things that are arranged in a smooth way, even when a visual ‘gap’.
  54. 54. Law of ClosureWe group things that are enclosed in visual shapes.
  55. 55. Law of SymmetryWe assume the horizontal / vertical part is identical. Good for before/after comparisons...
  56. 56. •pattern  recognition •we  are  constantly  grouping  objects  based  on  color,   shape,  direction,  proximity,  closure/enclosure,  ... •so:  where  should  a  chart  legend  be  located?•  keep  it  ‘simple’ •we  like  simple,  close,  smooth,  symmetrical,  easy-­‐to-­‐ process  shapes...•design  accordingly •if  we  are  aware  of  these  laws  we  can  take  advantage   of  them  to  design  better  charts  or  dashboards.•be  careful •be  aware  of  their  negative  impact:  we  shouldn’t  force   the  reader  to  see  groups  that  aren’t  really  therehttp://www.excelcharts.com/blog/data-visualization-excel-users/gestalt-laws/
  57. 57. Context influences visual perception!
  58. 58. 71Context influences visual perception!
  59. 59. Context influences visual perception!
  60. 60. Data. 2. understanding properties of data
  61. 61. “Concrete” Data. carries spatial layout. position, color, visual characteristics. represented by graphical reproduction
  62. 62. “Abstract” Data. data without natural representation. requires metaphor to be perceived. data is “mapped” in visual form
  63. 63. Information Visualization?
  64. 64. Information Visualization (Scientific) VisualizationExamples Examplesstock market, friend clouds, microscopic events,network, DNA functions, ... human organs, ...Data Characteristics Data CharacteristicsAbstract ConcreteMulti-dimensional 2 or 3 dimensional Mostly time-dependentRequirements. RequirementsVisual metaphor 3D and fast renderingUser interaction User interactionFocus: Exploration Focus: Analysisthen Analysis then Explorationthen Presentation then Presentation
  65. 65. Kinds of data...S Stevens “On the theory of scales and measurements” (1946)
  66. 66. Quantitative Data. numerical, scalar values, arithmetic operations,... . e.g. 4, 14, 5445453, 2, 1.00342, 3, ... 83
  67. 67. Ordinal Data. larger / smaller than... . e.g. Monday, Tuesday, Wednesday, ... 84
  68. 68. Categorical / Hierarchical Data. can be categorized . e.g. alphabetically, thematically, functionality . e.g. desktop folder hierarchy, work hierarchy, ... 85
  69. 69. Network / Relational Data. one-to-many, many-to-one relationships . often these are ‘weighted’ . e.g. social networks, people working on projects, ... 86
  70. 70. Nominal / Unstructured Data. no order, no units, only “equal or different” . e.g. Australia, Belgium, Mexico 87
  71. 71. Temporal / Dynamic Data. time dependent . related to progress of time, history, ... . e.g. stock market, news stories, sensor readings,... 88
  72. 72. § attributes § dimensions § variables § columns§ values § quantitative § ordinal § categorical § nominal§ items § tuples § datapoints § rows
  73. 73. § dimensionality § # of attributes§ scale / size § # of items§ value range § bits/value, min/max, etc.§ time dependency?
  74. 74. “The current complexity of data is staggering, and our ability to collect data is increasing at a faster rate that our ability to analyse it.”Data Complexity
  75. 75. How is data ‘complex’?. size: number of records. dimensionality: number of attributes. time-dependency: data changes over time
  76. 76. Data is more complex now?. complexity of human society always increasing. quantity always increasing, never decreasing!. speed of data creation always increasing
  77. 77. An Inconvenient Truth - Al Gore
  78. 78. 1. Social relevance - An Inconvenient Truth - 2006
  79. 79. An Inconvenient Truth - Al Gore
  80. 80. Complexity of data: The ‘real’ data behind “An Inconvenient Truth - Al Gore”
  81. 81. Data Mapping. 3. understanding how to map data to an image
  82. 82. Why mapping necessary?
  83. 83. because the data is abstract... “the challenge is to invent new metaphors for presenting information & developing ways to manipulate these metaphors to make sense out of the information...”Information Visualization ‘Design’ Challenge
  84. 84. Data + data mapping = visual representation that can be (relatively fairly) interpreted
  85. 85. data insight 10010110 knowledge transfer data mapping mapping inversion visualisation comprehension ! visual transferData Mapping Methodology
  86. 86. data §data mapping requirements? § computable 10010110 § no user interaction required § algorithm: data -> value data § comprehensible mapping § user understands § intuitively, within short time visualisation § invertible § mapping backwards from § form to exact data value 103Data Mapping Methodology
  87. 87. Mon Sun Mon Sun15 17 19 15 22 10 15 15 17 19 15 22 10 1515 10 11 15 20 12 18 15 10 11 15 20 12 1814 23 12 15 18 12 17 14 23 12 15 18 12 1713 11 21 10 29 12 17 13 11 21 10 29 12 1729 12 22 12Mon Sun Mon Sun15 17 19 15 22 10 15 15 17 19 15 22 10 1515 10 11 15 20 12 18 15 10 11 15 20 12 1814 23 12 15 18 12 17 14 23 12 15 18 12 1713 11 21 10 29 12 17 13 11 21 10 29 12 1722 12 22 12 0 35 0 35 20 29 10 12Including Visual Perception / Dynamic Queries / Pattern Exploration
  88. 88. 15 17 19 15 22 10 15 15 17 19 15 18 10 15 15 17 19 15 11 10 15 15 10 11 15 20 12 18 15 10 11 15 16 12 18 15 10 11 15 08 12 18 14 23 12 15 18 12 17 23 23 27 34 32 29 27 14 15 12 15 18 12 17 13 11 21 10 17 12 30 13 11 21 10 29 12 30 13 11 15 10 10 12 10 22 12 22 12 11 12 0 35 20 29 0 35 20 29 0 35 20 29Increasing Visual Bandwidth / Visual Abstraction / Increasing Data Density
  89. 89. Example Movie Dataset: How to approach the data mapping?
  90. 90. 1 items data ê ??? 2 attributes data ê ???Data CharacteristicsFrom abstract data to visual form
  91. 91. 1 data items ê visual objects 2 data attributes ê visual object propertiesData MappingFrom abstract data to visual form
  92. 92. objects point, line, area, volume, ... properties position size, length, area, volume orientation, angle, slope color, texture, transparency shape animation, time, blinkVisualization “Language”From abstract data to visual form
  93. 93. Example scatterplot of movie dataset Year → X Length → Y Popularity → size Subject → color Award? → shape
  94. 94. Data Mapping Limitations. data scale: one object for each tuple?. data dimensionality: visual cue for each attribute?. value range: metaphorical range?. time dependency: can metaphor change?
  95. 95. (Cleveland and McGill)Guideline: Assign most important data pattern the most perceptual accurate visual cue.
  96. 96. Visual objects versus visual attributes.http://understandinggraphics.com/visualizations/information-display-tips/
  97. 97. What chart type to choose?http://extremepresentation.typepad.com/blog/2006/09/choosing_a_good.html
  98. 98. Intuitive data mapping....Less intuitive data mapping....
  99. 99. Context influences color perception!
  100. 100. http://www.nationalgeographic.nl/community/foto/bekijken/slow-motion-2Motion as Visual Cue. pre-attentive feature, can be added in ‘parallel’. simple action perceived as sophisticated behavior. attracts attention, enjoyable, motivates, .... technology available: e.g. graphics, screen estate
  101. 101. "An Experimental Study of Apparent Behaviour" (1944)Fritz Heider & Marianne Simmel
  102. 102. Simple motion, complex interpretation.http://www.biomotionlab.ca/Demos/BMLwalker.html
  103. 103. Motion properties
  104. 104. “‘Graphical Excellence’ is that which gives to the viewer a great number of ideas in the shortest time, with the least ink, in the smallest space”. “show data variation”, not “design variation”. communication: clarity, precision, efficiency. simplicity of design - complexity of data
  105. 105. French Invasion of Russia (Minard, +-1864)Napoleon Retreat (Minard, +-1864)
  106. 106. temperaturetimetemp[day]
  107. 107. longitudelatitudearmy[size, day]army[position, day]
  108. 108. 1. Show comparisons, contrasts, differences2. Show causality, mechanism, explanation,systematic structure3. Show multivariate data; that is, show morethan 1 or 2 variables4. Completely integrate words, numbers, images,diagrams5.Thoroughly describe the evidence: title, authorsand sponsors, data sources, add measurementscales, highlight relevant issues6.Analytical presentations ultimately stand or falldepending on the quality, relevance andintegrity of their contentPrinciples for the Analysis and Presentation of Data - Tufte
  109. 109. 1. Show comparisons, contrasts,differences
  110. 110. 2. Show causality, mechanism, explanation, systematic structureFrench Invasion of Russia (Minard, +-1864)Napoleon Retreat (Minard, +-1864)
  111. 111. 3. Show multivariate data; that is, show more than 1 or 2 variablesFrench Invasion of Russia (Minard, +-1864)Napoleon Retreat (Minard, +-1864)
  112. 112. 4. Completely integrate words, numbers, images, diagramsFrench Invasion of Russia (Minard, +-1864)Napoleon Retreat (Minard, +-1864)
  113. 113. 5.Thoroughly describe the evidence: title, authors and sponsors, data sources, add measurement scales, highlight relevant issuesFrench Invasion of Russia (Minard, +-1864)Napoleon Retreat (Minard, +-1864)
  114. 114. 6.Analytical presentations ultimately stand or fall depending on the quality, relevance and integrity of their contentFrench Invasion of Russia (Minard, +-1864)Napoleon Retreat (Minard, +-1864)
  115. 115. Telling a narrative with a “simple” design, though still “complex” data
  116. 116. Chart Components
  117. 117. 1. Lie Factor2. Data Ink Ratio3. Data Density4. Chart Junk5. ExpressivenessPrinciples for the Analysis and Presentation of Data - Tufte
  118. 118. Lie FactorExaggeration of differences between values
  119. 119. Lie FactorExaggeration of differences between values
  120. 120. 1. Lie FactorQuantifying the exaggeration of differences between values
  121. 121. 1. Lie FactorExaggeration of differences between values
  122. 122. 1. Lie FactorHow many times can 1978 fit into 1958?
  123. 123. 2. Data-Ink Ratio2. Data-Ink RatioUnnecessary & distracting patterns - graphics emphasizing style over information
  124. 124. 2. Data-Ink Ratio2. Data-Ink RatioUnnecessary & distracting patterns - graphics emphasizing style over information
  125. 125. Five Laws of “Data-Ink”• Above all else show (only) the data• Maximize the data-ink ratio• Erase non-data ink• Erase redundant data-ink• Revise and edit2. Data-Ink RatioUnnecessary & distracting patterns - graphics emphasizing style over information
  126. 126. 2. Data-Ink RatioUnnecessary & distracting gridlines - graphics emphasizing style over information
  127. 127. 2. Data-Ink RatioUnnecessary & distracting patterns - graphics emphasizing style over information
  128. 128. 3. Data Density“A pixel is a terrible thing to waste” Ben Shneiderman
  129. 129. 3. Data Density“A pixel is a terrible thing to waste” Ben Shneiderman
  130. 130. 4. Chart JunkQuestion: Is the data still visible without the graphics?How much can you take away before it becomes illegible?
  131. 131. 4. Chart JunkIs the data still visible without the graphics?
  132. 132. 4. Chart JunkIs the data still visible without the graphics?
  133. 133. 4. Chart JunkIs the data still visible without the graphics?
  134. 134. 4. Chart junkAvoid non-data ink (fonts, lines, aesthetics) Growing wealth and declining ODA 
  135. 135. 4. Chart junkAvoid non-data ink (fonts, lines, aesthetics)
  136. 136. 4. Chart junkAvoid non-data ink (fonts, lines, aesthetics)
  137. 137. 5. ExpressivenessEncode the data - Encode only the data
  138. 138. 5. ExpressivenessEncode the data - Encode only the data
  139. 139. 5. ExpressivenessUse line charts for time-series or continuous data, never for categorical data.
  140. 140. 5. ExpressivenessEncode the data - Encode only the data
  141. 141. 5. ExpressivenessEncode the data - Encode only the data (and never use 3D on a 2D medium).
  142. 142. 5. ExpressivenessEncode the data - Encode only the data
  143. 143. 5. ExpressivenessEncode the data - Encode only the data
  144. 144. 5. ExpressivenessWrite out explanations on the graphic itself.
  145. 145. 5. ExpressivenessWrite out explanations on the graphic itself.
  146. 146. 5. ExpressivenessAdd labels next to graph (then no legend required!)
  147. 147. 5. ExpressivenessScaling: lines should cover 2/3 of data area.... (contentious issue!)
  148. 148. 5. ExpressivenessColors: avoid high contrast
  149. 149. 5. ExpressivenessColors: avoid high contrast
  150. 150. 5. ExpressivenessColors: choose harmonious colors
  151. 151. 5. ExpressivenessColors: for related set of data attributes, use similar colors.
  152. 152. 5. ExpressivenessColors: use color for highlighting. Note difference if data attributes are related or not!
  153. 153. 5. ExpressivenessOrientation text horizontally. Order by size, not by alphabet.
  154. 154. 5. ExpressivenessComparing vertical axesjunkcharts.typepad.com only when identical...
  155. 155. 5. ExpressivenessComparing vertical axesjunkcharts.typepad.com only when identical, or at least similar rate (e.g. 2x baseline)
  156. 156. 5. ExpressivenessAvoid size as quantitativejunkcharts.typepad.com value. If so, map values as surface area (and never as radius!)
  157. 157. 5. ExpressivenessAvoid size as quantitativejunkcharts.typepad.com value. If so, map values as surface area (and never as radius!)
  158. 158. 5. ExpressivenessMaps only useful for spatial distribution. They do not take into account population densityjunkcharts.typepad.com(which explains trends), physical size of districts (which are visually more prominent),...
  159. 159. 5. ExpressivenessAvoid donut charts...
  160. 160. junkcharts.typepad.com
  161. 161. China, Egypt, Mexico, South Africa, Philippines, India - and for different periods.
  162. 162. Homework- Find a recent,“Belgian” infographic- Analyze: dataset, data mapping, ...- Critique (good/bad) design decisions- Propose redesign + explain why- Show example, critique, redesign- Send 1 PDF file to...
  163. 163. Thank you! Questions?------.----------@asro.kuleuven.be /// http://infosthetics.com /// @infosthetics

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