Storytelling With Data

Data Visualization MeetUpAngela Cornelius

Pittsburgh May 12, 2014

Monday, May 12, 14

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

Who Are You Writing For?

• Know Your Audience

• Speak in Their Voice

• Design for Their Preferences

Monday, May 12, 14

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

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

ProcessBen 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

2 Approaches to Storytelling

•Idea - Research - Render•Research - Idea - Render

Monday, May 12, 14

Issue DrivenStory to Datahttp://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

Character-DrivenData 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

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

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

StyleWhat Sets You Apart

• Individual Expression

• Design that Invokes an Unique Feeling

• Specific Use of Design Principles & Elements

• A Brand

• Your Signature

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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

Relationships

• Show the Relationships

• Correlation & Causation

• How do the Relationships Impact Story?

• Compare & Contrast

Monday, May 12, 14

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

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

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

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

And Don’t Forget...

• Labels (Axis)

• Double-Check Geometry/Math

• Include Your Sources

• Consider Your Audience

Monday, May 12, 14

Monday, May 12, 14

Visualization Types (Reference Systems)

1. Charts: No reference system—e.g., Wordle.com, pie charts2. 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

Monday, May 12, 14

Monday, May 12, 14

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

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