Storytelling with Data

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Presentation given to Pittsburgh Data Vis Meetup on May 12, 2014. About approaching data visualization from a storytelling.

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Storytelling with Data

  1. 1. Storytelling With Data Data Visualization MeetUp Angela Cornelius Pittsburgh May 12, 2014 Monday, May 12, 14
  2. 2. Once Upon A Time... • What is a Story? • Narrative, Information, Idea, Moral • What Makes a Good Storyteller/Journalist? • Plot Context, Theme, Characters • Structure • Linear: InOrganic, Beginning, Middle, End • NonLinear: Organic, No Set Order • Climax - The Ah-Ha Moment • All Material Supports, Not Distracting, Focused • Form • Book, eBook, Bowser, App, Interactive • Poem, Essay, Memoir, Novel... • Graph, Chart, Map, Network... Monday, May 12, 14
  3. 3. Who Are You Writing For? • Know Your Audience • Speak in Their Voice • Design for Their Preferences Monday, May 12, 14
  4. 4. How to Tell A (Good) Story • Select Topic • Know Your Audience • Collect Material- Research, Read • Create an Outline, Mindmap, Storyboard, Wireframe • Write A Draft- Once, Twice, Three Times • Focus on Key Points - Use Effective Titles • Emphasize Primary Message • Express - Emotional Response, Take-Away • Entertaining, Informative, Compelling Monday, May 12, 14
  5. 5. Steps to Story • What Questions are you Asking? • What Story are you Telling? • Find Where to Get Answers- Research • Draft Ideas into Structure • Write, Refine Writing, Edit, Encode • Add Details to Emphasize • Show Don’t Tell, Variety of Views • Refine / Focus Monday, May 12, 14
  6. 6. Process Ben Fry • Acquire • Obtain the data • Parse • Structure data’s meaning, order it in table • Filter • Cleanse all but what is relevant • Mine • Discern Patterns • Represent • Basic Visual Models • Refine • Focus on Key Points • Interact • Feature Controls Monday, May 12, 14
  7. 7. 2 Approaches to Storytelling •Idea - Research - Render •Research - Idea - Render Monday, May 12, 14
  8. 8. Issue Driven Story to Data http://www.gapminder.org/ • Author is Present • An Opinion is Being Expressed, Subjective • The Data is Focuses on Single POV/ Opinion of the Author • Data is Collected to Support Story • A Single Primary Climax/Message Monday, May 12, 14
  9. 9. Character-Driven Data to Story http://eyeofestival.com/ http://flowingdata.com/category/visualization/artistic-visualization/ • Let the Data be the Character • Develop the Data = Character Development • The Data Tells the Story as it Unfolds, Objective • Data can be very Complex & have Several ‘Meanings/Interpretations’ • Anyone may be able to modify, no single author • Create Experience • Code to Collect Data, Story in the Data • No Single Beginning, Ending, or Climax Monday, May 12, 14
  10. 10. Essential Essay Elements • Voice - Personal Presence of the Author • Engagement between Self & World • Authors Self Exploration/Discovery • Need to Show & Tell • Why Investigating Something & What to Realize Form it, Structure of Engagement, Context • Veracity/Authenticity • Mutability of Form • Multi-tasking amorphousness, user friendly • Sense of Intellectual Plot, Moral, Quest, Engagement, or Payoff Monday, May 12, 14
  11. 11. Starting A Story • What is Surprising? • Is there Tension? Where? • What Should Be - and What Actually is • Is there Conflict? • Where do these ideas/issues/people/ collide? • Does the beginning set up a context, a conundrum, a search? • Are there Problems, Dilemmas? • What is Unusual, different from what is expected? • What contradictions are present? • What does the Scene look like? Monday, May 12, 14
  12. 12. Style What Sets You Apart • Individual Expression • Design that Invokes an Unique Feeling • Specific Use of Design Principles & Elements • A Brand • Your Signature Monday, May 12, 14
  13. 13. Story Time • What Changes Over Time? • How Does it Change? • Why is the Change Interesting to the Story? • How Can I Best Show the Change? Monday, May 12, 14
  14. 14. Relationships • Show the Relationships • Correlation & Causation • How do the Relationships Impact Story? • Compare & Contrast Monday, May 12, 14
  15. 15. Check Your Facts • In good Journalism, the Facts need to be Correct, In good Data Vis, the Facts need to be Correct too. • Verify - compare several sources • Cleansing Data, look for errors, zeros deleted, typo, etc. Monday, May 12, 14
  16. 16. Questions = Answers (or at least clues to how to visualize) • At What Level is the Visualization? • Individual POV? • Micro - small data sets 1-100 • Group POV? • Meso - group between 100-10,000 records • Global POV? • Macro - exceed 10,000 records Monday, May 12, 14
  17. 17. Questions = Answers (or at least clues to how to visualize) • What Kind of Question Am I Asking? • Statistical Analysis/Profiling • When? = Temporal • Where? = Geospatial • What? = Topical • With Whom? = Network Monday, May 12, 14
  18. 18. Just as in Story, Data is is Best Understood within Context • Use a Key / Legend • Decode what You Encoded • Give Context • Tell Level of Data, if not obvious • Show Time Frame • Region / Coverage Area • Kind of Topic • Type of Network Monday, May 12, 14
  19. 19. And Don’t Forget... • Labels (Axis) • Double-Check Geometry/Math • Include Your Sources • Consider Your Audience Monday, May 12, 14
  20. 20. Monday, May 12, 14
  21. 21. Visualization Types (Reference Systems) 1. Charts: No reference system—e.g., Wordle.com, pie charts 2. Tables: Categorical axes that can be selected, reordered; cells can be color coded and might contain proportional symbols. Special kind of graph. 3. Graphs: Quantitative or qualitative (categorical) axes. Timelines, bar graphs, scatter plots. 4. Geospatial maps: Use latitude and longitude reference system. World or city maps. 5. Network graphs: Node position might depends on node attributes or node similarity. Tree graphs: hierarchies, taxonomies, genealogies. Networks: social networks, migration flows. Monday, May 12, 14
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  24. 24. Go Tell Create Stories With Data! http://www.ted.com/talks/ hans_rosling_shows_the_best_stats_you_ve_ever_seen Monday, May 12, 14
  25. 25. References • Visual Insights, Katy Borner & David Polley, IVMooc, Indiana University, http://ivmooc.cns.iu.edu/ • Flowing Data, http://flowingdata.com/ • eyeo http://eyeofestival.com/ • Visualize This, The FlowigData Guide to Design Visualization, and Statistics, Nathan Yau • Visualizing Data, Ben Fry • Visual Complexity, Mapping Patterns of Information, Manuel Lima Monday, May 12, 14

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