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

1. Storytelling
With Data
Data Visualization MeetUp
Angela Cornelius
Pittsburgh May 12, 2014
Monday, May 12, 14

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. Who Are You Writing For?
• Know Your Audience
• Speak in Their Voice
• Design for Their Preferences
Monday, May 12, 14

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. 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. 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. 2 Approaches to Storytelling
•Idea - Research - Render
•Research - Idea - Render
Monday, May 12, 14

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. 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. 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. 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. 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. 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. Relationships
• Show the Relationships
• Correlation & Causation
• How do the Relationships Impact Story?
• Compare & Contrast
Monday, May 12, 14

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. 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. 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. 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. And Don’t Forget...
• Labels (Axis)
• Double-Check Geometry/Math
• Include Your Sources
• Consider Your Audience
Monday, May 12, 14

20. Monday, May 12, 14

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

22. Monday, May 12, 14

23. Monday, May 12, 14

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. 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