intro to information visualization

1,225 views

Published on

Introduction for course on information visualization.

Published in: Education
0 Comments
1 Like
Statistics
Notes
  • Be the first to comment

No Downloads
Views
Total views
1,225
On SlideShare
0
From Embeds
0
Number of Embeds
1
Actions
Shares
0
Downloads
32
Comments
0
Likes
1
Embeds 0
No embeds

No notes for slide

intro to information visualization

  1. 1. HUMAN COMPUTER INTERACTION LAB INFORMATION VISUALISATION capita selecta 17/10/2012 Joris Klerkx @jkofmskWednesday 17 October 12
  2. 2. Imagine you never saw a car... Would the following definitions help to explain it? http://www.thefreedictionary.com/carWednesday 17 October 12
  3. 3. Imagine you never saw a car... Would the following definitions help to explain it? http://www.thefreedictionary.com/car 1. It’s an automobileWednesday 17 October 12
  4. 4. Imagine you never saw a car... Would the following definitions help to explain it? http://www.thefreedictionary.com/car 1. It’s an automobile A phone that automatically takes a call..Wednesday 17 October 12
  5. 5. Imagine you never saw a car... Would the following definitions help to explain it? http://www.thefreedictionary.com/car 1. It’s an automobile A phone that automatically takes a call.. 2. It’s a vehicle, such as a streetcarWednesday 17 October 12
  6. 6. Imagine you never saw a car... Would the following definitions help to explain it? http://www.thefreedictionary.com/car 1. It’s an automobile A phone that automatically takes a call.. 2. It’s a vehicle, such as a streetcarWednesday 17 October 12
  7. 7. Imagine you never saw a car... Would the following definitions help to explain it? http://www.thefreedictionary.com/car 1. It’s an automobile A phone that automatically takes a call.. 2. It’s a vehicle, such as a streetcar 3. It’s a boxlike enclosure for passengers, with wheelsWednesday 17 October 12
  8. 8. Imagine you never saw a car... Would the following definitions help to explain it? http://www.thefreedictionary.com/car 1. It’s an automobile A phone that automatically takes a call.. 2. It’s a vehicle, such as a streetcar 3. It’s a boxlike enclosure for passengers, with wheelsWednesday 17 October 12
  9. 9. Imagine you never saw a car... Would the following definitions help to explain it? http://www.thefreedictionary.com/car 1. It’s an automobile A phone that automatically takes a call.. 2. It’s a vehicle, such as a streetcar 3. It’s a boxlike enclosure for passengers, with wheels 4. A chariot, carriage, or cartWednesday 17 October 12
  10. 10. Imagine you never saw a car... Would the following definitions help to explain it? http://www.thefreedictionary.com/car 1. It’s an automobile A phone that automatically takes a call.. 2. It’s a vehicle, such as a streetcar 3. It’s a boxlike enclosure for passengers, with wheels 4. A chariot, carriage, or cartWednesday 17 October 12
  11. 11. Imagine you never saw a car... Would the following definitions help to explain it? http://www.thefreedictionary.com/car 1. It’s an automobile A phone that automatically takes a call.. 2. It’s a vehicle, such as a streetcar 3. It’s a boxlike enclosure for passengers, with wheels 4. A chariot, carriage, or cart A picture is worth a 1000 wordsWednesday 17 October 12
  12. 12. A definition... Information Visualisation is the use of interactive visual representations to amplify cognition [Card. et. al]Wednesday 17 October 12
  13. 13. A definition... Information Visualisation is the use of interactive visual representations to amplify cognition [Card. et. al] Find out what a data set is about What are the stories behind the data? Communicating data Facilitate human interaction for exploration and understanding Empower people to make informed decisionsWednesday 17 October 12
  14. 14. Not new..Wednesday 17 October 12
  15. 15. Not new.. http://www.datavis.ca/milestones/Wednesday 17 October 12
  16. 16. Not new.. http://www.datavis.ca/milestones/Wednesday 17 October 12
  17. 17. Publication Networks in conferences Who are the most prolific author(s)? Who is co-authoring with who?Wednesday 17 October 12
  18. 18. Publication Networks in conferences Who are the most prolific author(s)? Who is co-authoring with who?Wednesday 17 October 12
  19. 19. Publication Networks in conferences Who are the most prolific author(s)? Who is co-authoring with who?Wednesday 17 October 12
  20. 20. Publication Networks in conferences Who are the most prolific author(s)? Who is co-authoring with who?Wednesday 17 October 12
  21. 21. Student Activity Meter How are my students working? When do they work? Are there students in trouble? ...Wednesday 17 October 12
  22. 22. Student Activity Meter How are my students working? When do they work? Are there students in trouble? ...Wednesday 17 October 12
  23. 23. Student Activity Meter How are my students working? When do they work? Are there students in trouble? ...Wednesday 17 October 12
  24. 24. Step up! Make students aware about their activity in the courseWednesday 17 October 12
  25. 25. MUSE - Visualizing the origins and connections of institutions based on co-authorship of publications Nagel, T., Duval, E.: Muse:Visualizing the origins and connections of institutions based on co-authorship of publications. Science2.0 for TEL workshop at EC-TEL 2010, Barcelona, SpainWednesday 17 October 12
  26. 26. On the menu... graph Some design basics visualization How to design a visualisation (application)?Wednesday 17 October 12
  27. 27. What has the bigger share? ‘Real Estate’ or ‘Bonds’ has the bigger share? http://www.perceptualedge.com/Wednesday 17 October 12
  28. 28. What has the bigger share? ‘Real Estate’ or ‘Bonds’ has the bigger share? Size & angle are not preattentive http://www.perceptualedge.com/Wednesday 17 October 12
  29. 29. “Save the pies for dessert” S. Few What has the bigger share? ‘Real Estate’ or ‘Bonds’ has the bigger share? Size & angle are not preattentive http://www.perceptualedge.com/Wednesday 17 October 12
  30. 30. Verkiezingen 14/10/12Wednesday 17 October 12
  31. 31. deredactie.be Verkiezingen 14/10/12Wednesday 17 October 12
  32. 32. deredactie.be Verkiezingen 14/10/12 demorgen.beWednesday 17 October 12
  33. 33. deredactie.be Verkiezingen 14/10/12 demorgen.be vtm.beWednesday 17 October 12
  34. 34. deredactie.be Verkiezingen 14/10/12 demorgen.be vtm.beWednesday 17 October 12
  35. 35. CHECK YOUR DATAWednesday 17 October 12
  36. 36. CHECK YOUR DATA http://nieuws.vtm.be/verkiezingen/gemeente?province=P1&city=G73Wednesday 17 October 12
  37. 37. CHECK YOUR DATA http://nieuws.vtm.be/verkiezingen/gemeente?province=P1&city=G73Wednesday 17 October 12
  38. 38. COMMUNICATE THE CORRECT STORYWednesday 17 October 12
  39. 39. COMMUNICATE THE CORRECT STORY nieuwsblad.be vtm.be deredactie.beWednesday 17 October 12
  40. 40. DON’T USE VISUALISATIONS TO MISLEAD... BP - leak in gulf of mexico http://flowingdata.com/category/statistics/mistaken-data/Wednesday 17 October 12
  41. 41. DON’T USE VISUALISATIONS TO MISLEAD... BP - leak in gulf of mexico http://flowingdata.com/category/statistics/mistaken-data/Wednesday 17 October 12
  42. 42. DON’T USE VISUALIZATIONS TO LIE... (1/2) http://www.perceptualedge.com/ http://flowingdata.com/category/statistics/mistaken-data/Wednesday 17 October 12
  43. 43. DON’T USE VISUALIZATIONS TO LIE... (1/2) http://www.perceptualedge.com/ http://flowingdata.com/category/statistics/mistaken-data/Wednesday 17 October 12
  44. 44. DON’T USE VISUALIZATIONS TO LIE... (1/2) http://www.perceptualedge.com/ http://flowingdata.com/category/statistics/mistaken-data/Wednesday 17 October 12
  45. 45. DON’T USE VISUALIZATIONS TO LIE... (1/2) http://www.perceptualedge.com/ http://flowingdata.com/category/statistics/mistaken-data/Wednesday 17 October 12
  46. 46. DON’T USE VISUALIZATIONS TO LIE... (2/2) http://flowingdata.com/category/statistics/mistaken-data/Wednesday 17 October 12
  47. 47. DON’T USE VISUALIZATIONS TO LIE... (2/2) http://flowingdata.com/category/statistics/mistaken-data/Wednesday 17 October 12
  48. 48. USE COMMON SENSE (1/3) Which of these line graphs is easier to read? http://www.perceptualedge.com/Wednesday 17 October 12
  49. 49. USE COMMON SENSE (2/3) Which of these two tables is easier to read? http://www.perceptualedge.com/Wednesday 17 October 12
  50. 50. USE COMMON SENSE (3/3) Which labels are easier to read? http://www.perceptualedge.com/Wednesday 17 October 12
  51. 51. Choose graphs that best communicates your data or answer your questions about your data Which graph makes it easier to focus on the pattern of change through time, instead of the individual values? http://www.perceptualedge.com/Wednesday 17 October 12
  52. 52. THINK ABOUT WHAT YOU DO Seems ok? http://www.perceptualedge.com/Wednesday 17 October 12
  53. 53. THINK ABOUT WHAT YOU DO Seems ok? http://www.perceptualedge.com/Wednesday 17 October 12
  54. 54. THINK ABOUT WHAT YOU DO Seems ok? Equal interval scale http://www.perceptualedge.com/Wednesday 17 October 12
  55. 55. Which graph makes it easier to determine R&Ds travel expense? http://www.perceptualedge.com/Wednesday 17 October 12
  56. 56. Which graph makes it easier to determine R&Ds travel expense? BE CAREFUL WITH 3D (DON’T USE IT) http://www.perceptualedge.com/Wednesday 17 October 12
  57. 57. On the menu... Some graph design basics visualization How to design a visualisation (application)?Wednesday 17 October 12
  58. 58. 2 Facts to keep in mindWednesday 17 October 12
  59. 59. 2 Facts to keep in mindHumans have advanced perceptual abilitiesWednesday 17 October 12
  60. 60. 2 Facts to keep in mindHumans have advanced perceptual abilities Our brains makes us extremely good at recognizing visual patternsWednesday 17 October 12
  61. 61. 2 Facts to keep in mindHumans have advanced perceptual abilities Our brains makes us extremely good at recognizing visual patterns Humans have little short term memoryWednesday 17 October 12
  62. 62. 2 Facts to keep in mindHumans have advanced perceptual abilities Our brains makes us extremely good at recognizing visual patterns Humans have little short term memory Our brains remember relatively little of what we perceiveWednesday 17 October 12
  63. 63. 2 Facts to keep in mindHumans have advanced perceptual abilities Our brains makes us extremely good at recognizing visual patterns Make Use of Gestalt principles Humans have little short term memory Our brains remember relatively little of what we perceiveWednesday 17 October 12
  64. 64. 2 Facts to keep in mindHumans have advanced perceptual abilities Our brains makes us extremely good at recognizing visual patterns Make Use of Gestalt principles Make it interactive, provide visual help Humans have little short term memory Our brains remember relatively little of what we perceiveWednesday 17 October 12
  65. 65. THE VISUALIZATION PIPELINEWednesday 17 October 12
  66. 66. Step 1: Think of a dataset, Formulate the questionsWednesday 17 October 12
  67. 67. Step 1: Think of a dataset, Formulate the questions “where” “when’’ “how much” “how often” (“why”)Wednesday 17 October 12
  68. 68. Step 1: Think of a dataset, Formulate the questions “where” “when’’ “how much” “how often” (“why”) Who are your intended users?Wednesday 17 October 12
  69. 69. Example data-set : Facebook privacy statement Offer precise controls for sharing on the Internet...Wednesday 17 October 12
  70. 70. Example data-set : Facebook privacy statement Offer precise controls for sharing on the Internet... Users should navigate through 50 settings with more than 170 optionsWednesday 17 October 12
  71. 71. Example data-set : Facebook privacy statement Offer precise controls for sharing on the Internet... Users should navigate through 50 settings with more than 170 options Questions?Wednesday 17 October 12
  72. 72. Example data-set : Facebook privacy statement Offer precise controls for sharing on the Internet... Users should navigate through 50 settings with more than 170 options Questions? How did it change over time?Wednesday 17 October 12
  73. 73. Example data-set : Facebook privacy statement Offer precise controls for sharing on the Internet... Users should navigate through 50 settings with more than 170 options Questions? How did it change over time? How does it compare to privacy statements of other tools?Wednesday 17 October 12
  74. 74. Example data-set : Facebook privacy statement Offer precise controls for sharing on the Internet... Users should navigate through 50 settings with more than 170 options Questions? How did it change over time? How does it compare to privacy statements of other tools? What are the options?Wednesday 17 October 12
  75. 75. Wednesday 17 October 12
  76. 76. Step 2: Gather the datasetWednesday 17 October 12
  77. 77. Step 2: Gather the dataset eg. open data, census.gov, NY Times API, etcWednesday 17 October 12
  78. 78. Step 2: Gather the dataset eg. open data, census.gov, NY Times API, etc Define the characteristics of the dataWednesday 17 October 12
  79. 79. Step 2: Gather the dataset eg. open data, census.gov, NY Times API, etc Define the characteristics of the data Time? hierarchical? 1D? 2D? nD? network data?Wednesday 17 October 12
  80. 80. Step 2: Gather the dataset eg. open data, census.gov, NY Times API, etc Define the characteristics of the data Time? hierarchical? 1D? 2D? nD? network data? scales?Wednesday 17 October 12
  81. 81. Step 2: Gather the dataset eg. open data, census.gov, NY Times API, etc Define the characteristics of the data Time? hierarchical? 1D? 2D? nD? network data? scales? https://www.facebook.com/about/privacyWednesday 17 October 12
  82. 82. Step 3: Apply a visual mappingWednesday 17 October 12
  83. 83. Step 3: Apply a visual mapping Encode data characteristics into visual formWednesday 17 October 12
  84. 84. Step 3: Apply a visual mapping Encode data characteristics into visual form Simplicity is the ultimate sophistication. Leonardo da VinciWednesday 17 October 12
  85. 85. Size most commonly used (?)Wednesday 17 October 12
  86. 86. Colors used for identifying patterns & anomalies in big datasets Color Principles - Hue, Saturation, and ValueWednesday 17 October 12
  87. 87. Gestalt Principles ¡ Law  of    Proximity The closer objects are to each other, the more likely they are to be perceived as a group (Ehrenstein, 2004) http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentationWednesday 17 October 12
  88. 88. Gestalt Principles ¡ Law  of    Proximity The closer objects are to each other, the more likely they are to be perceived as a group (Ehrenstein, 2004) http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentationWednesday 17 October 12
  89. 89. Gestalt Principles ¡ Law  of    Proximity The closer objects are to each other, the more likely they are to be perceived as a group (Ehrenstein, 2004) ¡ Law  of  Symmetry Objects must be balanced or symmetrical to be seen as complete or whole (Chang, 2002). http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentationWednesday 17 October 12
  90. 90. Gestalt Principles ¡ Law  of    Proximity The closer objects are to each other, the more likely they are to be perceived as a group (Ehrenstein, 2004) ¡ Law  of  Symmetry Objects must be balanced or symmetrical to be seen as complete or whole (Chang, 2002). http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentationWednesday 17 October 12
  91. 91. Gestalt Principles ¡ Law  of    Similarity Objects that are similar, with like components or attributes are more likely to be organised together (Schamber, 1986). Objects are viewed in vertical rows because of their similar attributes. http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentationWednesday 17 October 12
  92. 92. Gestalt Principles ¡ Law  of    Similarity Objects that are similar, with like components or attributes are more likely to be organised together (Schamber, 1986). Objects are viewed in vertical rows because of their similar attributes. ¡ Law  of  Common  Fate Objects with a common movement, that move in the same direction, at the same pace , at the same time are organised as a group (Ehrenstein, 2004). http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentationWednesday 17 October 12
  93. 93. Gestalt Principles ¡ Law  of    Similarity Objects that are similar, with like components or attributes are more likely to be organised together (Schamber, 1986). Objects are viewed in vertical rows because of their similar attributes. ¡ Law  of  Common  Fate Objects with a common movement, that move in the same direction, at the same pace , at the same time are organised as a group (Ehrenstein, 2004). http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentationWednesday 17 October 12
  94. 94. Gestalt Principles ¡ Law  of    Similarity Objects that are similar, with like components or attributes are more likely to be organised together (Schamber, 1986). Objects are viewed in vertical rows because of their similar attributes. ¡ Law  of  Common  Fate Objects with a common movement, that move in the same direction, at the same pace , at the same time are organised as a group (Ehrenstein, 2004). http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentationWednesday 17 October 12
  95. 95. Gestalt Principles ¡ Law  of    Continuation Objects will be grouped as a whole if they are co-linear, or follow a direction (Chang, 2002; Lyons, 2001). http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentationWednesday 17 October 12
  96. 96. Gestalt Principles ¡ Law  of    Continuation Objects will be grouped as a whole if they are co-linear, or follow a direction (Chang, 2002; Lyons, 2001). ¡ Law  of  Isomorphism Is similarity that can be behavioural or perceptual, and can be a response based on the viewers previous experiences (Luchins & Luchins, 1999; Chang, 2002). This law is the basis for symbolism (Schamber, 1986). http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentationWednesday 17 October 12
  97. 97. Gestalt Principles ¡ Law  of    Continuation Objects will be grouped as a whole if they are co-linear, or follow a direction (Chang, 2002; Lyons, 2001). ¡ Law  of  Isomorphism Is similarity that can be behavioural or perceptual, and can be a response based on the viewers previous experiences (Luchins & Luchins, 1999; Chang, 2002). This law is the basis for symbolism (Schamber, 1986). http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentation There are more!Wednesday 17 October 12
  98. 98. Step 3: Apply a visual mapping Shape - circles, rectangles, stars, icons,.. Location - maps Network -node-link graphs Time - animations ...Wednesday 17 October 12
  99. 99. HOW DID IT CHANGE OVER TIME? http://www.nytimes.com/interactive/2010/05/12/business/facebook-privacy.htmlWednesday 17 October 12
  100. 100. HOW DID IT CHANGE OVER TIME? http://www.nytimes.com/interactive/2010/05/12/business/facebook-privacy.htmlWednesday 17 October 12
  101. 101. HOW DID IT CHANGE OVER TIME? http://www.nytimes.com/interactive/2010/05/12/business/facebook-privacy.htmlWednesday 17 October 12
  102. 102. HOW DOES FB COMPARE TO STATEMENTS OF OTHER TOOLS? http://www.nytimes.com/interactive/2010/05/12/business/facebook-privacy.htmlWednesday 17 October 12
  103. 103. HOW DOES FB COMPARE TO STATEMENTS OF OTHER TOOLS? http://www.nytimes.com/interactive/2010/05/12/business/facebook-privacy.htmlWednesday 17 October 12
  104. 104. WHAT ARE THE OPTIONS?Wednesday 17 October 12
  105. 105. Which visual encodings do you see? Example... London Tube MapWednesday 17 October 12
  106. 106. Which visual encodings do you see? Example... London Tube MapWednesday 17 October 12
  107. 107. e.g. sketch on paper e.g. what kind of filtering mechanisms?Wednesday 17 October 12
  108. 108. Step 3: Apply a visual mapping to your dataset e.g. sketch on paper e.g. what kind of filtering mechanisms?Wednesday 17 October 12
  109. 109. Step 3: Apply a visual mapping to your dataset e.g. sketch on paper Step 4: Think about interaction of visualisation app e.g. what kind of filtering mechanisms?Wednesday 17 October 12
  110. 110. Step 5: How to evaluate visualisations? Build Usable & Useful VisualisationsWednesday 17 October 12
  111. 111. Step 5: How to evaluate visualisations? Typical HCI metrics don’t always work that well •time required to learn the system •time required to achieve a goal •error rates •retention of the use of the interface over timeWednesday 17 October 12
  112. 112. Step 5: How to evaluate visualisations? Not so easy: how to measure improved insights? Typical HCI metrics don’t always work that well •time required to learn the system •time required to achieve a goal •error rates •retention of the use of the interface over timeWednesday 17 October 12
  113. 113. Step 5: How to evaluate visualisations? Not so easy: how to measure improved insights? Typical HCI metrics don’t always work that well •time required to learn the system •time required to achieve a goal •error rates •retention of the use of the interface over timeWednesday 17 October 12
  114. 114. Some metrics that can be usedWednesday 17 October 12
  115. 115. Some metrics that can be used • Effectiveness - does the visualization answer your questions? does it provide value? Do they provide new insight? How? Why? • Efficiency - to what extend may the visualization communicate your data to the users efficiently? Do they get quicker answers to their questions? • Usability - how easily the users interact with the system? Are the information provided in clear and understandable format? Eg. Do the layouts of elements make sense? • Usefulness - are the visualizations useful? How may the users benefit from it? • Functionality - to what extend does the application provides the functionalities required by the users?Wednesday 17 October 12
  116. 116. Rapid Prototyping Time Iteration 1 Iteration 2 Iteration 3 Iteration N ... • Design focus on usefulness & usability • target personas & scenarios • Evaluate ideas in short iteration cycles • e.g draw boundary box vs. contour of object of interest • Evaluate in real-life settings • with real users 44Wednesday 17 October 12
  117. 117. Think aloud Usability lab Eye-tracking questionnaires (SUS, TAM, ...)Wednesday 17 October 12
  118. 118. Go outside your research lab Evaluate in real-life settings 46Wednesday 17 October 12
  119. 119. Go outside your research lab Evaluate in real-life settings Ec-tel 2010 Figure 4: Setting of the evaluation. Hypertext 2011 Overview first, search & filter, Start with what you know, details on demand then grow 46Wednesday 17 October 12
  120. 120. To conclude..Wednesday 17 October 12
  121. 121. To conclude..Wednesday 17 October 12
  122. 122. To conclude.. Lets try to bust 2 myths in this course...Wednesday 17 October 12
  123. 123. To conclude.. Lets try to bust 2 myths in this course... Visualisations are just cool graphicsWednesday 17 October 12
  124. 124. To conclude.. Lets try to bust 2 myths in this course... Visualisations are just cool graphics Graphics part of bigger picture of what stories to communicate & howWednesday 17 October 12
  125. 125. To conclude.. Lets try to bust 2 myths in this course... Visualisations are just cool graphics Graphics part of bigger picture of what stories to communicate & how Only experts can create good visualizationsWednesday 17 October 12
  126. 126. To conclude.. Lets try to bust 2 myths in this course... Visualisations are just cool graphics Graphics part of bigger picture of what stories to communicate & how Only experts can create good visualizations Maybe faster, but there are simple techniques anyone can applyWednesday 17 October 12
  127. 127. POINTERS • http://wearecolorblind.com/articles/quick-tips/ • http://infosthetics.com • http://www.visualcomplexity.com/vc/ • http://bestario.org/research/remap • ... (a lot more online! )Wednesday 17 October 12
  128. 128. LIBRARIES • D3.js • http://www.jerryvermanen.nl/datajournalismlist/ • Processing • http://wiki.okfn.org/OpenVisualisation • http://flare.prefuse.org/ • http://iv.slis.indiana.edu/sw/ • http://abeautifulwww.com/2008/09/08/20-useful-visualization-libraries/ • Tableau software • R • Multitouch4J • Manyeyes... • ...Wednesday 17 October 12
  129. 129. FURTHER READINGS • “Readings in Information Visualization: Using Vision to Think”, Card, S et al • “Now i see”, “Show Me the Numbers”, Few, S. • “Beautiful Evidence”, Tufte, E. • “Information Visualization. Perception for design”, Ware, C. • Beautiful Visualization: Looking at Data through the Eyes of Experts (Theory in Practice): Julie Steele, Noah IliinskyWednesday 17 October 12
  130. 130. THANK YOU FOR YOUR ATTENTION! joris.klerkx@cs.kuleuven.be @jkofmsk 52Wednesday 17 October 12

×