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Visualization Lecture - Clariah Summer School 2018

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Lecture given at Clariah Summer School 2018 (on 5 July).

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Visualization Lecture - Clariah Summer School 2018

  1. 1. University of Oslo Library Hugo C. Huurdeman, 4 July, 2018 Clariah Summer School 
 Visualization lecture
  2. 2. This lecture’s program • 0. A little bit of context (5 min) • 1. Introduction to visualization (25 min) • 2. The visualization process (10 min) • 3. Supportive tools for visualization (15 min) • 4. A practical example using a Media Suite export (30 min) • After the coffee break, Carlos continues with visualization in Jupyter notebooks
  3. 3. 0. A little bit of context
  4. 4. Mikaela Aamodt | Dan Michael Heggø | Hugo Huurdeman | Helge Mjelde | Live Rasmussen | Heidi Rustad | Kyrre Låberg | Nina Thodesen University of Oslo Library bit.ly/VisualNavigationProject
  5. 5. Stream 2: Physical Interaction • Stream 1 & 3 build on top of existing work and infrastructure • Approach Stream 2: experiment with novel ways of interaction in physical space • with library’s book collections • experiments with a touch table (Science Library) • Includes an INF2260 project & INF Master project Yaron Okun Visualiza- tion (1) Picture: Marina Tofting
  6. 6. Clusters 
 color: representations of websites, size: number of crawls Example w/Dutch Web archive
  7. 7. Clusters 
 color: representations of websites, size: number of crawls Example w/Dutch Web archive
  8. 8. Word Clouds
 size: number of sites Example w/Dutch Web archive
  9. 9. Bar Charts
 color: unesco category, size: avg change % Example w/Dutch Web archive
  10. 10. Bar Charts
 color: unesco category, size: avg change % Example w/Dutch Web archive
  11. 11. Network (Force-directed)
 connetions: unesco category, size: number of crawls Example w/Dutch Web archive
  12. 12. Network (Force-directed)
 connetions: unesco category, size: number of crawls Example w/Dutch Web archive
  13. 13. 1. Introduction to visualization
  14. 14. Minard - Depicting events Russian military campaign by Napoleon (1812-13) https://www.edwardtufte.com/tufte/minard
  15. 15. Saving lives: tracing the source of a cholera outbreak in Soho, London (1854)
 map used to find cause of epidemic. Dr. John Snow https://www1.udel.edu/johnmack/frec682/cholera/
  16. 16. Provide insights into scientific data: climate change “hockey stick” graph Michael E. Mann, CC-BY, https://upload.wikimedia.org/wikipedia/commons/0/0a/Mann_hockeystick.jpg
  17. 17. Visualization? • “A visualization is any kind of visual representation of information designed to enable communication, analysis, discovery, exploration, etc.” (Cairo, 2016)
  18. 18. Visualization? • “A visualization is any kind of visual representation of information designed to enable communication, analysis, discovery, exploration, etc.” (Cairo, 2016) • “The representation and presentation of data to facilitate understanding (Kirk, 2016)
  19. 19. Visualization types? • Davis (2009) distinguishes the following types of visualization: • Statistical visualizations e.g. Supreme Court Justices • Infographics e.g. An internet minute • Maps e.g. New York Times immigration explorer • Network visualizations e.g. Social network analysis visualization • Artistic visualizations (“data as art”) e.g. “Forest of Numbers”
  20. 20. https://www.flickr.com/photos/idvsolutions/8806668702/sizes/o/in/photostream/
  21. 21. When to use visualization? • Can be used throughout research process
  22. 22. When to use visualization? • Can be used throughout research process • initial exploration, get a grasp (exploratory)
  23. 23. When to use visualization? • Can be used throughout research process • initial exploration, get a grasp (exploratory)
  24. 24. When to use visualization? • Can be used throughout research process • initial exploration, get a grasp (exploratory) • as an artefact of ongoing research (discovery) • i.e. “as process”
  25. 25. When to use visualization? • Can be used throughout research process • initial exploration, get a grasp (exploratory) • as an artefact of ongoing research (discovery) • i.e. “as process”
  26. 26. When to use visualization? • Can be used throughout research process • initial exploration, get a grasp (exploratory) • as an artefact of ongoing research (discovery) • i.e. “as process” • as an end product (explanatory) • i.e. “as product / outcome”
  27. 27. (Cleveland 1985, as cited in Spence, 2016) “Graphing data needs to be iterative because we often do not know what to expect of the data; a graph can help discover unknown aspects of the data, and once the unknown is known, we frequently find ourselves formulating new questions about the data.”
  28. 28. Qualities of visualizations • Cairo (2016) suggests a number of qualities of visualizations (which are often not met in practice!)
  29. 29. Qualities of visualizations • Cairo (2016) suggests a number of qualities of visualizations (which are often not met in practice!) • Functional It should depict data accurately, but also be useful to people
  30. 30. Qualities of visualizations • Cairo (2016) suggests a number of qualities of visualizations (which are often not met in practice!) • Functional It should depict data accurately, but also be useful to people • Beautiful A visualization should be ‘attractive’ to different audiences
  31. 31. Qualities of visualizations • Cairo (2016) suggests a number of qualities of visualizations (which are often not met in practice!) • Functional It should depict data accurately, but also be useful to people • Beautiful A visualization should be ‘attractive’ to different audiences • Insightful It should reveal evidence that we could have missed without the visualization
  32. 32. Qualities of visualizations • Cairo (2016) suggests a number of qualities of visualizations (which are often not met in practice!) • Functional It should depict data accurately, but also be useful to people • Beautiful A visualization should be ‘attractive’ to different audiences • Insightful It should reveal evidence that we could have missed without the visualization • Enlightening A visualization may “change our minds” (hopefully for the better…)
  33. 33. Qualities of visualizations • Cairo (2016) suggests a number of qualities of visualizations (which are often not met in practice!) • Functional It should depict data accurately, but also be useful to people • Beautiful A visualization should be ‘attractive’ to different audiences • Insightful It should reveal evidence that we could have missed without the visualization • Enlightening A visualization may “change our minds” (hopefully for the better…) • Truthful A visualization should depict truthful and honest research
  34. 34. https://flowingdata.com/2012/08/06/fox-news-continues-charting-excellence/ Enlightening?
  35. 35. Fallacies of visualization • Designing an understandable and reliable visualization is far from straightforward. • Importance of the origins, quality and scope of underlying data. • I.e., it is essential to understand the whole picture • Data critique: how was data generated? Is it complete? etc.
  36. 36. Numberofmoviereleases
  37. 37. Amountofchocolatesold
  38. 38. Numberofreceivedbooks
  39. 39. Numberofreceivedbooks Problems of storytelling, patternicity and confirmation (Cairo, 2016)
  40. 40. Problems of visualization • Patternicity • “Detecting interesting patterns, regardless of whether or not they are real” • Storytelling • Trying to find cause-effect relationships for patterns we observe • Confirmation • Confirming our own beliefs (cognitive dissonance, confirmation bias) • (Cairo, 2016)
  41. 41. How to pick the right chart? • How many variables to show in the chart? One, two, three, many? • Few or many data points? • Display values over period of time, or among items or groups? • Source: eazybi.com, extremepresentation.com
  42. 42. extremepresentation.typepad.com/blog/2006/09/choosing_a_good.html
  43. 43. datavizcatalogue.com
  44. 44. Storytelling • Which audience are you making the chart for? • E.g. scientific context, popular science, newspaper, website, etc. • What is your key message? • Which (combination of) text and charts can best tell this story?
  45. 45. Moritz Stefaner, http://rhythm-of-food.net/
  46. 46. https://www.economist.com/graphic-detail/2015/05/12/seeking-safety
  47. 47. https://interaktiv.morgenpost.de/zugezogene-in-berlin/
  48. 48. https://www.nytimes.com/interactive/2014/upshot/dialect-quiz-map.html
  49. 49. https://www.bloomberg.com/graphics/infographics/graphic-language-the-curse-of-the-ceo.html
  50. 50. https://www.bloomberg.com/graphics/infographics/graphic-language-the-curse-of-the-ceo.html
  51. 51. And: less is more … Cairo (2016) eazybi.com/blog/data_visualization_and_chart_types/ Some Do’s & Don’ts
  52. 52. 2. The visualization process
  53. 53. (Simplified) Steps data wrangling data enrichment visualizationcorpus creation
  54. 54. Title Text http://www.thomaspadilla.org/2015/02/17/8020rule/
  55. 55. Title Text http://www.thomaspadilla.org/2015/02/17/8020rule/
  56. 56. (Simplified) Steps data wrangling data enrichment visualizationcorpus creation * From 7 stages in data visualization (Fry, 2007) Iterative! Involves analysis along the way
  57. 57. (Simplified) Steps data wrangling data enrichment visualizationcorpus creation * From 7 stages in data visualization (Fry, 2007) Iterative! Involves analysis along the way
  58. 58. (Simplified) Steps data wrangling data enrichment visualizationcorpus creation Represent* Refine* Interact* Clean Parse* Filter* Mine*Acquire* Understand * From 7 stages in data visualization (Fry, 2007) Iterative! Involves analysis along the way
  59. 59. (Simplified) Steps data wrangling data enrichment visualizationcorpus creation Represent* Refine* Interact* Clean Parse* Filter* Mine*Acquire* Understand E.g. get data about
 acquired books * From 7 stages in data visualization (Fry, 2007) Iterative! Involves analysis along the way
  60. 60. (Simplified) Steps data wrangling data enrichment visualizationcorpus creation Represent* Refine* Interact* Clean Parse* Filter* Mine*Acquire* Understand E.g. clean up publisher 
 names E.g. get data about
 acquired books * From 7 stages in data visualization (Fry, 2007) Iterative! Involves analysis along the way
  61. 61. (Simplified) Steps data wrangling data enrichment visualizationcorpus creation Represent* Refine* Interact* Clean Parse* Filter* Mine*Acquire* Understand E.g. clean up publisher 
 names E.g. get geocodes 
 for placenames E.g. get data about
 acquired books * From 7 stages in data visualization (Fry, 2007) Iterative! Involves analysis along the way
  62. 62. (Simplified) Steps data wrangling data enrichment visualizationcorpus creation Represent* Refine* Interact* Clean Parse* Filter* Mine*Acquire* Understand E.g. clean up publisher 
 names E.g. get geocodes 
 for placenames E.g. project publisher
 locations onto map E.g. get data about
 acquired books * From 7 stages in data visualization (Fry, 2007) Iterative! Involves analysis along the way
  63. 63. Parse remove unnecessary fields
 create additional fields clean up publisher names create ‘pivot tables’, explore countsUnderstand Clean Filter data wrangling data enrichment visualization
  64. 64. Mine E.g. get geocodes for placenames data wrangling data enrichment visualization
  65. 65. Represent Refine Interact data wrangling data enrichment visualization
  66. 66. 3. Supportive tools for visualization
  67. 67. OpenRefine data wrangling data enrichment visualization
  68. 68. OpenRefine data wrangling data enrichment visualization
  69. 69. Google Spreadsheets data wrangling data enrichment visualization
  70. 70. Google Spreadsheets data wrangling data enrichment visualization
  71. 71. plot.ly data wrangling data enrichment visualization
  72. 72. plot.ly data wrangling data enrichment visualization
  73. 73. Google Fusion Tables Anat Ben David & Hugo Huurdeman (2013) data wrangling data enrichment visualization
  74. 74. Google Fusion Tables data wrangling data enrichment visualization
  75. 75. Tableau (Public) data wrangling data enrichment visualization
  76. 76. DMI tools data wrangling data enrichment visualization
  77. 77. Gephi visualizationdata wrangling data enrichment
  78. 78. Gephi 2011_12_Assad_09_EN.rtf2011_10_Assad_5_EN.rtf2011_11_Assad_15_EN.rtf2011_09_Assad_31_EN.rtf2011_08_Assad_26_EN.rtf2011_07_Assad_21_EN.rtf2011_06_Assad_22_EN.rtf2011_05_Assad_14_EN.rtf2011_04_Assad_100_EN.rtf2011_03_Assad_21_EN.rtf2011_01_Assad_58_EN.txt2011_02_Assad_29_EN.rtf london united nations iraq eu new zealandtwitter hosni mubarak president bashar al-assadlibyan capital tripoli monday nu.nl deaths spokesman syria the netherlands country peopleparty president tahrir square egypt mubarak regime historical eventnu.nl egypt historic event nu.nl press conference president hosni mubarak muslim brotherhood netherlandshamapercent CO-WORD ANALYSIS 2011 Detail Co-word anlysis of Assad articles: J 2011 A.BenDavid,A.Helmond,H.Huurdeman,D.Moats, N.Sánchez,T.Sammar,C.Somzé,DMIWinterSchool2013 visualizationdata wrangling data enrichment
  79. 79. RAW visualizationdata wrangling data enrichment
  80. 80. RAW visualizationdata wrangling data enrichment
  81. 81. d3 visualizationdata wrangling data enrichment
  82. 82. TimelineJS visualizationdata wrangling data enrichment
  83. 83. TimelineJS visualizationdata wrangling data enrichment Up next in the exercise
  84. 84. Jupyter notebooks data wrangling data enrichment visualization
  85. 85. Jupyter notebooks data wrangling data enrichment visualization Later this morning
  86. 86. 4. A practical exercise Exporting data from Media Suite, import into Google Sheets, 
 create timeline in TimelineJS
  87. 87. Go to: bit.ly/ClariahVisualizationTutorial for the links, data and a screencast
  88. 88. First step • 1. Open a browser window, preferably Chrome: • 2. From the Google Doc folder (bit.ly/ClariahVisualizationTutorial), 
 open the document First-step • 3. Open 4 browser tabs: • Tab 1: Google Sheets: sheets.google.com • Tab 2: ImportJSON for Google Sheets: github.com/bradjasper/ImportJSON • Tab 3: TimelineJS: timeline.knightlab.com/ • Tab 4: From the Google Doc folder (bit.ly/ClariahVisualizationTutorial), 
 double click on metros-bookmarks-basic-final.json
  89. 89. Data diggin’ & visualization • Ready? • Note: time is a bit limited • If you cannot keep up, there is a screencast of the process in the Google Doc folder, so you can try it out later
  90. 90. Any questions? ?
  91. 91. Literature and other pointers • Cairo (2016). The Truthful Art - Data, Charts, and Maps for Communication. • Davis, (2009). Article on visualization in a library context: inthelibrarywiththeleadpipe.org/2009/not-just-another-pretty-picture • Fry (2007), Visualizing Data, O’Reilly. • Kirk (2016). Data Visualization - A handbook for Data Driven Design • Nussbaumer Knaflic (2015). Storytelling with Data. Wiley. • Tufte (1983). The Visual Display of Quantitative Information. • Visualization examples: informationisbeautiful.net • Pivot tables: en.wikipedia.org/wiki/Pivot_table • Chart usage guidelines: eazybi.com/blog/data_visualization_and_chart_types • Improving the ‘data-ink ratio’: darkhorseanalytics.com/blog/data-looks-better-naked • Google Fusion Tables tutorials: https://support.google.com/fusiontables/answer/184641
  92. 92. University of Oslo Library Presentation by Hugo C. Huurdeman, 4 July, 2018 Clariah Summer School 
 Visualization lecture

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