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Andy Kirk's Facebook Talk

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Andy Kirk's Facebook Talk

  1. 1. Visualisation Workflow: Finding Stories and Telling Stories Andy Kirk
  2. 2. Hebden Bridge
  3. 3. Data Visualisation Blogger
  4. 4. Architect | Consultant
  5. 5. Trainer tion duct ion to DataVisualisa Training Courses Intro le Current Public Schedu Visualisation The Growth of Data le of 2012: e through to the midd publi c traini ng cours mean s for These are the scheduled ded us with ubiquitous Arts, Copenhagen | £250 COP2 in technology have provi e once data h Academy of Fine Exponential advances amounts of data. Wher Thu 8 Mar | Royal Danis Copenhagen | £250 COP1 mobi lising incredible mers have h Academy of Fine Arts, creati ng, recording and Our attitudes as consu Fri 9 Mar | Royal Danis London | £235 LON3 captured in abundance. for visual insight House, University of was scarce, now it is openn ess and yearn Thu 26 Apr | Senat e York City | £250 NY C1 nd transparency and ournalism, CUNY, New also evolved: we dema Fri 1 May | Grad Schoo 1 l of J n DC | £250 WDC1 to aid our understand ing. ation Center, Washingto £250 BAL1 for the Mon 1 May | Found 4 widespread capabilities Wed 1 May go | £250 CHI1 s to fantastic tools and iques requi red 6 Center Conference, Chica Yet, whilst we have acces knowledge and techn Fri 1 Jun | University 5 analysis of data, the Toron to | £250 TOR1 storage, handl ing and Mon 1 J | Venue TBC, 8 un £235 BRS 1 instin ct ach based on intuit ion, Fri 29 J un Edinburgh | £235 EDI1 e world, a design appro Hotel , University of a cluttered, competitiv Fri 6 J | Salisury Green ul 1 Amst erdam | £250 AMS data visualisation comes in. Fri 1 Jul | Venue TBC, 3 overload. This is where l A 1 discount 0% comm unications that appea Train ing page on and innovation, designing unleashing creati vity regist er to atten d an event . way our eyes and brain s process om where you can also exploiting the www.visualisingdata.c aimed at under standing and recen t times h in popularity over lisation and its growt sizes and The interest in data visua isations of all shapes, story. As a result , organ ster now to reserve a Places are limited so regi has been a remar kable value. ation of its poten tial waking up to the realis domain are now training workshop. place on your preferred tent Training Course Con Visit the www.visualising data.com, select the with a comprehensive, d location. The objective of the traini ng is to provi de delegates Training page and click on your preferre excitement event s buzzi ng with tion. You will leave the have acqui red, impact and ampli fy cogni ical capabilities you knowledge and pract rtunit ies about the foundation n challenges and oppo on future data visualisatio inspir ing you to take Further Information in the courses will include: environment main topics cover ed Class size a supportive learn ing The size is 20 to facilit ate of data visualisation The maxim um class and modern context Historical background n visual system en all attendees. of design and the huma group discussion betwe Foundation principles and select ion The essen tials of chart design and resources Refreshments tial visualisation tools centr al locati ons. Exploration of the essen process ed. All event s will be held in city n methodology and lunch will not be includ The visualisation desig n ing to visualisation desig Applying critical think itioners ice exam ples and pract Laptops Showcase of best pract s Visualisation project case studie lisation challenges g the day’s activi ties. re your own data visua across the group durin Opportunit ies to explo have a some devices Times ? end of the Who Should Attend time allocated at the g from 9:00 and extra regist ration comm encin r discussion s. nsibil ity for, or is intere sted in quest ions or hold furthe for anyone who has respo comm unicating data. session to pick up any The courses are suited and for visually exploring best pract ice approaches . Visualising Data Ltd body who lex datasets, or some st with large and comp t be an You might be an analy gement repor t. You migh the occasional mana visualisation just wants to enhance Ltd, a UK based data er of Visualising Data ber of this crowd. You might be a Andy Kirk is the found has been an active mem g to stand out from the traini ng service. He to adver tising and are lookin ner witho ut progr amm ing skills. design consultancy and design traini ng or a desig sector. progr amm er with no eering or the publi c lisingdata.com. cine, the media, engin popular blog www.visua You might be in medi we’ve all Data is everywhere and is no typical delegate. is most The point is that there Anyone and everyone with it, so let’s do it right. got to do something to atten d! welcome and encouraged
  6. 6. Trainer
  7. 7. Speaker
  8. 8. Author
  9. 9. CareerLancaster University | 1995 to 1999Degree in Operational Research + Year in IndustryCo-operative Insurance Society (CIS) | 1999 to 2001Business AnalystWest Yorkshire Police | 2001 to 2007Performance Analyst > Information ManagerUniversity of Leeds | 2007 to 2012Information ManagerUniversity of Leeds | 2007 to 2009Masters Degree (Research) in Data VisualisationVisualising Data Ltd. | 2010 to dateFreelance Jack of All Trades / Visualisation Mercenary
  10. 10. Visualisation Workflow: Finding Stories and Telling Stories
  11. 11. “The aggregation of marginal gains” Dave Brailsford
  12. 12. 1. Establish the visualisation‟s purpose and identify key factors2. Acquire, prepare and 3. Establish editorial focus explore your data with your subject matter 4. Conceive your visualisation design 5. Construct your data visualisation solution
  13. 13. 1. Establish the visualisation‟spurpose and identify key factors
  14. 14. What is „Purpose‟? Trigger Intent Its reason for existing The intended function and tone How well is it defined?Client project (brief)Internal project (brief) Self-initiated
  15. 15. Intent: Function Who does the work?Designer driven or Reader driven
  16. 16. Intent: Tone SciencArt e http://www.hybridtweaks.com/wp-content/uploads/2012/07/valuev-holyfield.jpg
  17. 17. Intent: ToneGetting [visualisation] right ismuch more a science than an art,which we can only achieve by studying human perception. Stephen Few http://www.interaction-design.org/encyclopedia/data_visualization_for_human_perception.html
  18. 18. Intent: ToneI have this fear that we aren’t feeling enough. Chris Jordan, TED Talk http://www.youtube.com/watch?v=f09lQ8Q1iKE&feature=youtu.be&t=5m11s
  19. 19. Intent: Function + ToneExploratory (Find Stories) Analytical/Pragmatic Explanatory (Tell Stories) Abstract/Emotive
  20. 20. Analytical +Exploratory
  21. 21. 512 Paths to the White House | New York Timeshttp://www.nytimes.com/interactive/2012/11/02/us/politics/paths-to-the-white-house.html
  22. 22. Analytical +Explanatory
  23. 23. Why Is Her Paycheck Smaller? | New York Timeshttp://www.nytimes.com/interactive/2009/03/01/business/20090301_WageGap.html http://www.barackobama.com/jobsrecord
  24. 24. Emotive/Abstract + Exploratory
  25. 25. OECD Better Life Index | Moritz Stefaner http://oecdbetterlifeindex.org/countries/united-kingdom/
  26. 26. Emotive/Abstract + Explanatory
  27. 27. What A Hundred Million Calls To 311 Reveal About New York | Pitch Interactive http://www.wired.com/magazine/2010/11/ff_311_new_york/
  28. 28. Potential Key FactorsThe aim? Open, strict, helpful, unhelpful, clarityPressures? Timescales, managerial, financialFormat? Static, interactive, video, toolsSetting? Issued report, presentedTechnical? Software, hardware, infrastructureAudience size?One, group, organisation, outsideAudience type? Domain, captive, generalResolution? Headlines, detailFrequency? One-off, regularRules? Structure, layout, style, colourPeople? Individual, team, the 8 hats…
  29. 29. 2. Acquire andprepare your data
  30. 30. The Hidden BurdenThe Hidden Cleverness
  31. 31. 80% perspiration,10% great idea, 10% output Simon Rogers The Guardian, „Facts Are Sacred: The Power of Data‟
  32. 32. 3. Establishingeditorial focus by finding stories
  33. 33. Good content reasonersand presenters are rare, designers are not. Edward Tufte http://adage.com/article/adagestat/edward-tufte-adagestat-q-a/230884/
  34. 34. Finding Stories
  35. 35. Finding Stories is…Using visualisation techniques to familiarise, learn about and discover insights from data
  36. 36. Graphical Literacy
  37. 37. Visual Analysis to Find StoriesComparisons – Categorical comparison and proportions – Ranking: big, small, medium – Measurements/values: absolutes – Range and distribution – Context: Targets, forecasts, averages – Hierarchical relationships
  38. 38. Visual Analysis to Find Stories: Comparisons
  39. 39. Visual Analysis to Find StoriesTrends and patterns (or lack of) – Up and down vs. flat? – Linear vs. exponential – Steady vs. fluctuating – Seasonal vs. random – Rate of change vs. steepness
  40. 40. Visual Analysis to Find Stories: Trends https://pbs.twimg.com/media/A8aptCHCAAAWyqx.png:large
  41. 41. Visual Analysis to Find StoriesRelationships – Outliers – Intersections – Correlations – Connections – Clusters – Associations – Gaps
  42. 42. Visual Analysis to Find Stories: Relationships
  43. 43. 4. Conceive yourvisualisation design
  44. 44. Telling [or Framing] Stories
  45. 45. Telling Stories is… Identifying and caring for thereader – taking responsibility tomaximise their potential insight
  46. 46. http://image.yaymicro.com/rz_1210x1210/0/5d9/pile-of-bricks-5d9ac1.jpg
  47. 47. http://yourcolorcoach.files.wordpress.com/2010/11/img_7704.jpg
  48. 48. http://degaryan.blogspot.com/2011/03/introduction.html
  49. 49. The Visualisation Anatomy
  50. 50. Data representation
  51. 51. Showing what we are trying to sayhttp://www.storytellingwithdata.com/2012/05/creating-visual-story-questions-to-ask.html
  52. 52. The Ebb and Flow of Movie Box Office Takings | New York Times http://www.nytimes.com/interactive/2008/02/23/movies/20080223_REVENUE_GRAPHIC.html
  53. 53. We rejected them because they didn’t do a good job of answering some of the mostinteresting questions... Different forms do better jobs at answering different questions. Amanda Cox (on NYT Stream Graph) http://www.portfolio.com/views/blogs/odd-numbers/2008/02/26/q-amp-a-anatomy-of-a-graphic
  54. 54. Comparing categories
  55. 55. Assessing hierarchies & part-to-whole relationships
  56. 56. Showing changes over time
  57. 57. Charting connections and relationships
  58. 58. Mapping geo-spatial data
  59. 59. Colour and background
  60. 60. Colour used well can enhance and clarify a presentation. Colour used poorly willobscure, muddle and confuse. Maureen Stone http://www.perceptualedge.com/articles/b-eye/choosing_colors.pdf
  61. 61. Confusion…http://go.bloomberg.com/multimedia/measuring-the-u-s-melting-pot/
  62. 62. OMGhttp://www-958.ibm.com/software/data/cognos/manyeyes/visualizations/schools-in-manchester-1821
  63. 63. To represent data values Colour (Hue)Colour (Saturation) http://www.theusrus.de/blog/the-good-the-bad-22012/
  64. 64. To distinguish between categorical items http://oecdbetterlifeindex.org/countries/united-kingdom/
  65. 65. To help distinguish foreground and background http://www.flickr.com/photos/walkingsf/6276642489/sizes/l/in/photostream/
  66. 66. To create signals/accentsFrom “Information Dashboard Design” and http://centerview.corda.com/corda/dashboards/examples/sales/main.dashxm l
  67. 67. Annotation
  68. 68. The annotation layer is themost important thing we do... otherwise it’s a case ofhere it is, you go figure it out. Amanda Cox, Graphics Editor, New York Times http://eyeofestival.com/speaker/amanda-cox/
  69. 69. TEDTalks “Myths about the developing world“ (2006) | Hans Rosling http://youtu.be/hVimVzgtD6w?t=1m1s
  70. 70. The Growth of Newspapers Across the US | Stanfordhttp://www.stanford.edu/group/ruralwest/cgi-bin/drupal/visualizations/us_newspapers
  71. 71. The Growth of Newspapers Across the US | Stanfordhttp://www.stanford.edu/group/ruralwest/cgi-bin/drupal/visualizations/us_newspapers
  72. 72. Arrangement
  73. 73. Consider the placement of every single visible element in a way that minimises thinking and maximises interpretation
  74. 74. Deliberate designhttp://www.perceptualedge.com/blog/wp-content/uploads/2012/10/dashboard-competition-winner.png
  75. 75. „Narrative Visualization: Telling Stories with Data‟, Edward Segel and Jeff Heer http://vis.stanford.edu/papers/narrative
  76. 76. 1. Magazine StyleDot point map of cholera deaths | Jon Snowhttp://www.casa.ucl.ac.uk/martin/msc_gis/map_making_myth_making.pdf
  77. 77. 2. Annotated ChartWhy Is Her Paycheck Smaller? | New York Timeshttp://www.nytimes.com/interactive/2009/03/01/business/20090301_WageGap.html
  78. 78. 3. Partitioned PosterSteroids or not, the pursuit is on | New York Times http://vis.stanford.edu/images/figures/case-bonds.png
  79. 79. 4. Flow ChartGraphic of Napoleons March (1869) | Charles Joseph Minard http://www.edwardtufte.com/tufte/posters
  80. 80. 5. Comic Strip Drought‟s footprint | New York Timeshttp://www.nytimes.com/interactive/2012/07/20/us/drought-footprint.html
  81. 81. 6. Slide Show Rise of the Megacities | The Guardianhttp://www.guardian.co.uk/global-development/interactive/2012/oct/04/rise-of-megacities-interactive
  82. 82. 7. Video/AnimationVisualizing how a population grows to 7 billion | NPRhttp://www.npr.org/2011/10/31/141816460/visualizing-how-a-population-grows-to-7-billion
  83. 83. Interactivity
  84. 84. Interactive Features and FunctionsVariable adjustment – selectionhighlighting/brushing, filtering, excluding, sortingView adjustment – pan, zoom, scale, rotate,transpose, arrange, tabsAnnotation – hovering/annotate, drop linesAnimation – play, pause, reset, chapternavigation, grab the slider, show newdata/changed data
  85. 85. Wind map | Fernanda Viegas and Martin Wattenberg http://hint.fm/wind/
  86. 86. http://www.normzarr.com/2010/05/22/midipad-ipad-iphone-music-app-wireless-touchscreen-software-controller-for-ableton-live-logic- cubase-nuendo/
  87. 87. 5. Construct and evaluate your datavisualisation solution
  88. 88. http://www.visualisingdata.com/index.php/resources/
  89. 89. Sample Project
  90. 90. Visualizing the London 2012 OlympicGames we will see the best of the best compete for pride,This summer,glory, and, of course, medals at the Olympics. From kilogramslifted in weightlifting to the number of individual countriescompeting to the number of medals won by competing nations - the Olympicsprovides a barrage of numbers that are ripe for designers to analyze andvisualize.We challenge you to use data and design to visualize the Olympics, helping usunderstand and enjoy as we watch. For instance, you could create a piece thatcontextualizes each country‟s medal count with information about theirpopulation, GDP, and athletic training resources. Or you could illuminate theresults of a particular event or the impact of hosting the London 2012 OlympicsGames on the UKs economy. “We‟re looking for any data-driven project that brings new insight, context, or comparison to our
  91. 91. Find stories…
  92. 92. Find stories…
  93. 93. Establish Narrative/Data QuestionsRepeat for all relevant sports and events:• Comparison between patterns for different medals?• What % improvement in time has there been?• Which events have improved the most and the least?• Comparison between progress of men and women? - Is one gender improving more than the other? - Any evidence of women getting closer to men?
  94. 94. Tell (or frame) stories…
  95. 95. Tell (or frame) stories… Data representation - line chart, dot plots, small multiples, tables Colour and background - gold, silver, bronze, blue, orange Animation and interaction - Data/view manipulation Arrangement - intro, selections, chart, filters, extra stats The annotation layer - context, annotated detail, stats
  96. 96. Construction
  97. 97. EvaluationUnderstanding (10 Points):How effectively does the visualization communicate?How well does it help you make sense of this issue?Originality (5 Points):Are the approach and design innovative?Style (5 Points):Is the visualization aesthetically compelling?
  98. 98. EvaluationAndy Kirks “The Pursuit of Faster” also earned an honorable mention for its level of data analysis that was unmatched by most, if not all, of the challenge entries. Its focused andthorough narrative further distinguished the project from the other interactive entries.
  99. 99. Learning the Craft
  100. 100. http://images.wikia.com/marvel_dc/images/9/93/Adventures_of_Superman_424.jpg | http://www.adobenido.com/blog/wp-content/uploads/2012/01/wonder_woman.jpg
  101. 101. http://collider.com/wp-content/uploads/WarGames-Sheedy-and-Broderick-on-computer.jpg
  102. 102. Multi-disciplinary: Art & Science
  103. 103. The 8 Hats of Data Visualisation ProjectInitiator Journalist Communicator ManagerCognitive Design Computer Data Science Science Science
  104. 104. CraftPractice, practice, practice – experience is the keySeek potential projects – paid, curiosity, contestsLearn about yourself – take notes, self critiqueTechnical skills – push yourself out of comfort zoneEvaluate others – silently or provide reviewsPublish yourself – encourage and digest peercritique
  105. 105. TheoryOnline content – immerse yourself in the communityBooks – so many invaluable references and inspirationsAcademia – papers, journalsConferences – within the field and around itTraining/education – look for good training provider…
  106. 106. www.visualisingdata.comandy@visualisingdata.com @visualisingdata

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