Visualisation Workflow:
  Finding Stories and
     Telling Stories

       Andy Kirk
Hebden Bridge
Data Visualisation Blogger
Architect | Consultant
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
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                                                         comm       unications that appea                                                                                     Train ing page on
                             and   innovation, designing
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                                    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
Trainer
Speaker
Author
Career
Lancaster University | 1995 to 1999
Degree in Operational Research + Year in Industry

Co-operative Insurance Society (CIS) | 1999 to 2001
Business Analyst

West Yorkshire Police | 2001 to 2007
Performance Analyst > Information Manager

University of Leeds | 2007 to 2012
Information Manager

University of Leeds | 2007 to 2009
Masters Degree (Research) in Data Visualisation

Visualising Data Ltd. | 2010 to date
Freelance Jack of All Trades / Visualisation Mercenary
Visualisation Workflow:
  Finding Stories and
     Telling Stories
“The aggregation of marginal gains”
         Dave Brailsford
1. Establish the
              visualisation‟s purpose
              and identify key factors

2. 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
1. Establish the
   visualisation‟s
purpose and identify
     key factors
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
Intent: Function



      Who does the work?

Designer driven or Reader driven
Intent: Tone
                                                                                    Scienc
Art
                                                                                      e




      http://www.hybridtweaks.com/wp-content/uploads/2012/07/valuev-holyfield.jpg
Intent: Tone

Getting [visualisation] right is
much 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
Intent: Tone



I 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
Intent: Function + Tone
Exploratory (Find Stories)        Analytical/Pragmatic




                                                         Explanatory (Tell Stories)
                                   Abstract/Emotive
Analytical
     +
Exploratory
512 Paths to the White House | New York Times




http://www.nytimes.com/interactive/2012/11/02/us/politics/paths-to-the-white-house.html
Analytical
     +
Explanatory
Why Is Her Paycheck Smaller? | New York Times




http://www.nytimes.com/interactive/2009/03/01/business/20090301_WageGap.html
                    http://www.barackobama.com/jobsrecord
Emotive/Abstract
       +
  Exploratory
OECD Better Life Index | Moritz Stefaner




   http://oecdbetterlifeindex.org/countries/united-kingdom/
Emotive/Abstract
       +
  Explanatory
What A Hundred Million Calls To 311 Reveal About New York | Pitch Interactive




                     http://www.wired.com/magazine/2010/11/ff_311_new_york/
Potential Key Factors
The aim? Open, strict, helpful, unhelpful, clarity
Pressures? Timescales, managerial, financial
Format? Static, interactive, video, tools
Setting? Issued report, presented
Technical? Software, hardware, infrastructure
Audience size?
One, group, organisation, outside
Audience type? Domain, captive, general
Resolution? Headlines, detail
Frequency? One-off, regular
Rules? Structure, layout, style, colour
People? Individual, team, the 8 hats…
2. Acquire and
prepare your data
The Hidden Burden

The Hidden Cleverness
80% perspiration,
10% great idea, 10% output
                  Simon Rogers




      The Guardian, „Facts Are Sacred: The Power of Data‟
3. Establishing
editorial focus by
 finding stories
Good content reasoners
and presenters are rare,
  designers are not.
                        Edward Tufte




   http://adage.com/article/adagestat/edward-tufte-adagestat-q-a/230884/
Finding Stories
Finding Stories is…




Using visualisation techniques to
  familiarise, learn about and
  discover insights from data
Graphical Literacy
Visual Analysis to Find Stories
Comparisons
  – Categorical comparison and proportions
  – Ranking: big, small, medium
  – Measurements/values: absolutes
  – Range and distribution
  – Context: Targets, forecasts, averages
  – Hierarchical relationships
Visual Analysis to Find Stories:
         Comparisons
Visual Analysis to Find Stories
Trends and patterns (or lack of)
  – Up and down vs. flat?
  – Linear vs. exponential
  – Steady vs. fluctuating
  – Seasonal vs. random
  – Rate of change vs. steepness
Visual Analysis to Find Stories: Trends




         https://pbs.twimg.com/media/A8aptCHCAAAWyqx.png:large
Visual Analysis to Find Stories
Relationships
  – Outliers
  – Intersections
  – Correlations
  – Connections
  – Clusters
  – Associations
  – Gaps
Visual Analysis to Find Stories:
        Relationships
4. Conceive your
visualisation design
Telling [or Framing]
       Stories
Telling Stories is…




 Identifying and caring for the
reader – taking responsibility to
maximise their potential insight
http://image.yaymicro.com/rz_1210x1210/0/5d9/pile-of-bricks-5d9ac1.jpg
http://yourcolorcoach.files.wordpress.com/2010/11/img_7704.jpg
http://degaryan.blogspot.com/2011/03/introduction.html
The Visualisation Anatomy
Data representation
Showing what we are
   trying to say



http://www.storytellingwithdata.com/2012/05/creating-visual-story-questions-to-ask.html
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
We rejected them because they
     didn’t do a good job of
  answering some of the most
interesting 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
Comparing categories
Assessing hierarchies & part-to-whole relationships
Showing changes over time
Charting connections and relationships
Mapping geo-spatial data
Colour and background
Colour used well can enhance
  and clarify a presentation.
   Colour used poorly will
obscure, muddle and confuse.
                       Maureen Stone



      http://www.perceptualedge.com/articles/b-eye/choosing_colors.pdf
Confusion…




http://go.bloomberg.com/multimedia/measuring-the-u-s-melting-pot/
OMG




http://www-958.ibm.com/software/data/cognos/manyeyes/visualizations/schools-in-manchester-1821
To represent data values

     Colour (Hue)


Colour (Saturation)




                http://www.theusrus.de/blog/the-good-the-bad-22012/
To distinguish between categorical items




         http://oecdbetterlifeindex.org/countries/united-kingdom/
To help distinguish foreground and background




         http://www.flickr.com/photos/walkingsf/6276642489/sizes/l/in/photostream/
To create signals/accents




From “Information Dashboard Design” and http://centerview.corda.com/corda/dashboards/examples/sales/main.dashxm l
Annotation
The annotation layer is the
most important thing we do...
   otherwise it’s a case of
here it is, you go figure it out.

   Amanda Cox, Graphics Editor, New York Times



              http://eyeofestival.com/speaker/amanda-cox/
TEDTalks “Myths about the developing world“ (2006) | Hans Rosling




                     http://youtu.be/hVimVzgtD6w?t=1m1s
The Growth of Newspapers Across the US | Stanford




http://www.stanford.edu/group/ruralwest/cgi-bin/drupal/visualizations/us_newspapers
The Growth of Newspapers Across the US | Stanford




http://www.stanford.edu/group/ruralwest/cgi-bin/drupal/visualizations/us_newspapers
Arrangement
Consider the placement of every
 single visible element in a way
   that minimises thinking and
     maximises interpretation
Deliberate design




http://www.perceptualedge.com/blog/wp-content/uploads/2012/10/dashboard-competition-winner.png
„Narrative Visualization: Telling Stories with
    Data‟, Edward Segel and Jeff Heer




               http://vis.stanford.edu/papers/narrative
1. Magazine Style
Dot point map of cholera deaths | Jon Snow




http://www.casa.ucl.ac.uk/martin/msc_gis/map_making_myth_making.pdf
2. Annotated Chart
Why Is Her Paycheck Smaller? | New York Times




http://www.nytimes.com/interactive/2009/03/01/business/20090301_WageGap.html
3. Partitioned Poster
Steroids or not, the pursuit is on | New York Times




         http://vis.stanford.edu/images/figures/case-bonds.png
4. Flow Chart
Graphic of Napoleon's March (1869) | Charles Joseph Minard




                 http://www.edwardtufte.com/tufte/posters
5. Comic Strip
     Drought‟s footprint | New York Times




http://www.nytimes.com/interactive/2012/07/20/us/drought-footprint.html
6. Slide Show
                 Rise of the Megacities | The Guardian




http://www.guardian.co.uk/global-development/interactive/2012/oct/04/rise-of-megacities-interactive
7. Video/Animation
Visualizing how a population grows to 7 billion | NPR




http://www.npr.org/2011/10/31/141816460/visualizing-how-a-population-grows-to-7-billion
Interactivity
Interactive Features and Functions
Variable adjustment – selection
highlighting/brushing, filtering, excluding, sorting

View adjustment – pan, zoom, scale, rotate,
transpose, arrange, tabs

Annotation – hovering/annotate, drop lines

Animation – play, pause, reset, chapter
navigation, grab the slider, show new
data/changed data
Wind map | Fernanda Viegas and Martin Wattenberg




                   http://hint.fm/wind/
http://www.normzarr.com/2010/05/22/midipad-ipad-iphone-music-app-wireless-touchscreen-software-controller-for-ableton-live-logic-
                                                      cubase-nuendo/
5. Construct and
 evaluate your data
visualisation solution
http://www.visualisingdata.com/index.php/resources/
Sample Project
Visualizing the London 2012 Olympic
Games we will see the best of the best compete for pride,
This summer,
glory, and, of course, medals at the Olympics. From kilograms
lifted in weightlifting to the number of individual countries
competing to the number of medals won by competing nations - the Olympics
provides a barrage of numbers that are ripe for designers to analyze and
visualize.

We challenge you to use data and design to visualize the Olympics, helping us
understand and enjoy as we watch. For instance, you could create a piece that
contextualizes each country‟s medal count with information about their
population, GDP, and athletic training resources. Or you could illuminate the
results of a particular event or the impact of hosting the London 2012 Olympics
Games on the UK's economy.


  “We‟re looking for any data-driven project that
 brings new insight, context, or comparison to our
Find stories…
Find stories…
Establish Narrative/Data Questions

Repeat 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?
Tell (or frame) stories…
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
Construction
Evaluation

Understanding (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?
Evaluation




Andy Kirk's “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 and
thorough narrative further distinguished
  the project from the other interactive
                  entries.
Learning the Craft
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
http://collider.com/wp-content/uploads/WarGames-Sheedy-and-Broderick-on-computer.jpg
Multi-disciplinary: Art & Science
The 8 Hats of Data Visualisation
                                        Project
Initiator   Journalist   Communicator
                                        Manager




Cognitive      Design       Computer      Data
 Science                     Science     Science
Craft
Practice, practice, practice – experience is the key

Seek potential projects – paid, curiosity, contests

Learn about yourself – take notes, self critique

Technical skills – push yourself out of comfort zone

Evaluate others – silently or provide reviews

Publish yourself – encourage and digest peer
critique
Theory
Online content – immerse yourself in the community


Books – so many invaluable references and inspirations


Academia – papers, journals


Conferences – within the field and around it


Training/education – look for good training provider…
www.visualisingdata.com
andy@visualisingdata.com
    @visualisingdata

Andy Kirk's Facebook Talk

  • 1.
    Visualisation Workflow: Finding Stories and Telling Stories Andy Kirk
  • 2.
  • 3.
  • 4.
  • 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.
  • 7.
  • 8.
  • 9.
    Career Lancaster University |1995 to 1999 Degree in Operational Research + Year in Industry Co-operative Insurance Society (CIS) | 1999 to 2001 Business Analyst West Yorkshire Police | 2001 to 2007 Performance Analyst > Information Manager University of Leeds | 2007 to 2012 Information Manager University of Leeds | 2007 to 2009 Masters Degree (Research) in Data Visualisation Visualising Data Ltd. | 2010 to date Freelance Jack of All Trades / Visualisation Mercenary
  • 10.
    Visualisation Workflow: Finding Stories and Telling Stories
  • 11.
    “The aggregation ofmarginal gains” Dave Brailsford
  • 12.
    1. Establish the visualisation‟s purpose and identify key factors 2. 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.
    1. Establish the visualisation‟s purpose and identify key factors
  • 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
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    Intent: Function Who does the work? Designer driven or Reader driven
  • 16.
    Intent: Tone Scienc Art e http://www.hybridtweaks.com/wp-content/uploads/2012/07/valuev-holyfield.jpg
  • 17.
    Intent: Tone Getting [visualisation]right is much 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
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    Intent: Tone I havethis fear that we aren’t feeling enough. Chris Jordan, TED Talk http://www.youtube.com/watch?v=f09lQ8Q1iKE&feature=youtu.be&t=5m11s
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    Intent: Function +Tone Exploratory (Find Stories) Analytical/Pragmatic Explanatory (Tell Stories) Abstract/Emotive
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    Analytical + Exploratory
  • 21.
    512 Paths tothe White House | New York Times http://www.nytimes.com/interactive/2012/11/02/us/politics/paths-to-the-white-house.html
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    Analytical + Explanatory
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    Why Is HerPaycheck Smaller? | New York Times http://www.nytimes.com/interactive/2009/03/01/business/20090301_WageGap.html http://www.barackobama.com/jobsrecord
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    Emotive/Abstract + Exploratory
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    OECD Better LifeIndex | Moritz Stefaner http://oecdbetterlifeindex.org/countries/united-kingdom/
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    Emotive/Abstract + Explanatory
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    What A HundredMillion Calls To 311 Reveal About New York | Pitch Interactive http://www.wired.com/magazine/2010/11/ff_311_new_york/
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    Potential Key Factors Theaim? Open, strict, helpful, unhelpful, clarity Pressures? Timescales, managerial, financial Format? Static, interactive, video, tools Setting? Issued report, presented Technical? Software, hardware, infrastructure Audience size? One, group, organisation, outside Audience type? Domain, captive, general Resolution? Headlines, detail Frequency? One-off, regular Rules? Structure, layout, style, colour People? Individual, team, the 8 hats…
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    The Hidden Burden TheHidden Cleverness
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    80% perspiration, 10% greatidea, 10% output Simon Rogers The Guardian, „Facts Are Sacred: The Power of Data‟
  • 32.
  • 33.
    Good content reasoners andpresenters are rare, designers are not. Edward Tufte http://adage.com/article/adagestat/edward-tufte-adagestat-q-a/230884/
  • 34.
  • 35.
    Finding Stories is… Usingvisualisation techniques to familiarise, learn about and discover insights from data
  • 36.
  • 37.
    Visual Analysis toFind Stories Comparisons – Categorical comparison and proportions – Ranking: big, small, medium – Measurements/values: absolutes – Range and distribution – Context: Targets, forecasts, averages – Hierarchical relationships
  • 38.
    Visual Analysis toFind Stories: Comparisons
  • 39.
    Visual Analysis toFind Stories Trends 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.
    Visual Analysis toFind Stories: Trends https://pbs.twimg.com/media/A8aptCHCAAAWyqx.png:large
  • 41.
    Visual Analysis toFind Stories Relationships – Outliers – Intersections – Correlations – Connections – Clusters – Associations – Gaps
  • 42.
    Visual Analysis toFind Stories: Relationships
  • 43.
  • 44.
  • 45.
    Telling Stories is… Identifying and caring for the reader – taking responsibility to maximise their potential insight
  • 46.
  • 47.
  • 48.
  • 49.
  • 50.
  • 51.
    Showing what weare trying to say http://www.storytellingwithdata.com/2012/05/creating-visual-story-questions-to-ask.html
  • 52.
    The Ebb andFlow of Movie Box Office Takings | New York Times http://www.nytimes.com/interactive/2008/02/23/movies/20080223_REVENUE_GRAPHIC.html
  • 53.
    We rejected thembecause they didn’t do a good job of answering some of the most interesting 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.
  • 55.
    Assessing hierarchies &part-to-whole relationships
  • 56.
  • 57.
  • 58.
  • 59.
  • 60.
    Colour used wellcan enhance and clarify a presentation. Colour used poorly will obscure, muddle and confuse. Maureen Stone http://www.perceptualedge.com/articles/b-eye/choosing_colors.pdf
  • 61.
  • 62.
  • 63.
    To represent datavalues Colour (Hue) Colour (Saturation) http://www.theusrus.de/blog/the-good-the-bad-22012/
  • 64.
    To distinguish betweencategorical items http://oecdbetterlifeindex.org/countries/united-kingdom/
  • 65.
    To help distinguishforeground and background http://www.flickr.com/photos/walkingsf/6276642489/sizes/l/in/photostream/
  • 66.
    To create signals/accents From“Information Dashboard Design” and http://centerview.corda.com/corda/dashboards/examples/sales/main.dashxm l
  • 67.
  • 68.
    The annotation layeris the most important thing we do... otherwise it’s a case of here it is, you go figure it out. Amanda Cox, Graphics Editor, New York Times http://eyeofestival.com/speaker/amanda-cox/
  • 69.
    TEDTalks “Myths aboutthe developing world“ (2006) | Hans Rosling http://youtu.be/hVimVzgtD6w?t=1m1s
  • 70.
    The Growth ofNewspapers Across the US | Stanford http://www.stanford.edu/group/ruralwest/cgi-bin/drupal/visualizations/us_newspapers
  • 71.
    The Growth ofNewspapers Across the US | Stanford http://www.stanford.edu/group/ruralwest/cgi-bin/drupal/visualizations/us_newspapers
  • 72.
  • 73.
    Consider the placementof every single visible element in a way that minimises thinking and maximises interpretation
  • 74.
  • 75.
    „Narrative Visualization: TellingStories with Data‟, Edward Segel and Jeff Heer http://vis.stanford.edu/papers/narrative
  • 76.
    1. Magazine Style Dotpoint map of cholera deaths | Jon Snow http://www.casa.ucl.ac.uk/martin/msc_gis/map_making_myth_making.pdf
  • 77.
    2. Annotated Chart WhyIs Her Paycheck Smaller? | New York Times http://www.nytimes.com/interactive/2009/03/01/business/20090301_WageGap.html
  • 78.
    3. Partitioned Poster Steroidsor not, the pursuit is on | New York Times http://vis.stanford.edu/images/figures/case-bonds.png
  • 79.
    4. Flow Chart Graphicof Napoleon's March (1869) | Charles Joseph Minard http://www.edwardtufte.com/tufte/posters
  • 80.
    5. Comic Strip Drought‟s footprint | New York Times http://www.nytimes.com/interactive/2012/07/20/us/drought-footprint.html
  • 81.
    6. Slide Show Rise of the Megacities | The Guardian http://www.guardian.co.uk/global-development/interactive/2012/oct/04/rise-of-megacities-interactive
  • 82.
    7. Video/Animation Visualizing howa population grows to 7 billion | NPR http://www.npr.org/2011/10/31/141816460/visualizing-how-a-population-grows-to-7-billion
  • 83.
  • 84.
    Interactive Features andFunctions Variable adjustment – selection highlighting/brushing, filtering, excluding, sorting View adjustment – pan, zoom, scale, rotate, transpose, arrange, tabs Annotation – hovering/annotate, drop lines Animation – play, pause, reset, chapter navigation, grab the slider, show new data/changed data
  • 85.
    Wind map |Fernanda Viegas and Martin Wattenberg http://hint.fm/wind/
  • 86.
  • 87.
    5. Construct and evaluate your data visualisation solution
  • 88.
  • 89.
  • 90.
    Visualizing the London2012 Olympic Games we will see the best of the best compete for pride, This summer, glory, and, of course, medals at the Olympics. From kilograms lifted in weightlifting to the number of individual countries competing to the number of medals won by competing nations - the Olympics provides a barrage of numbers that are ripe for designers to analyze and visualize. We challenge you to use data and design to visualize the Olympics, helping us understand and enjoy as we watch. For instance, you could create a piece that contextualizes each country‟s medal count with information about their population, GDP, and athletic training resources. Or you could illuminate the results of a particular event or the impact of hosting the London 2012 Olympics Games on the UK's economy. “We‟re looking for any data-driven project that brings new insight, context, or comparison to our
  • 91.
  • 92.
  • 93.
    Establish Narrative/Data Questions Repeatfor 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.
    Tell (or frame)stories…
  • 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
  • 97.
  • 98.
    Evaluation Understanding (10 Points): Howeffectively 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?
  • 99.
    Evaluation Andy Kirk's “ThePursuit 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 and thorough narrative further distinguished the project from the other interactive entries.
  • 100.
  • 101.
  • 102.
  • 103.
  • 104.
    The 8 Hatsof Data Visualisation Project Initiator Journalist Communicator Manager Cognitive Design Computer Data Science Science Science
  • 105.
    Craft Practice, practice, practice– experience is the key Seek potential projects – paid, curiosity, contests Learn about yourself – take notes, self critique Technical skills – push yourself out of comfort zone Evaluate others – silently or provide reviews Publish yourself – encourage and digest peer critique
  • 106.
    Theory Online content –immerse yourself in the community Books – so many invaluable references and inspirations Academia – papers, journals Conferences – within the field and around it Training/education – look for good training provider…
  • 107.

Editor's Notes

  • #14 With data so large and potentially complex, the importance of clarity of purpose, goals and analytical perspective is great.
  • #17 So we can classify the purposes but there is another dimension, that of ‘tone’.We mentioned earlier the battle between art and science…
  • #18 On one hand the ‘scientists’ believe…
  • #19 On the other hand, more creative and abstract practitioners are creating works that blow away the conventional bar charts…
  • #33 With data so large and potentially complex, the importance of clarity of purpose, goals and analytical perspective is great.
  • #35 With data so large and potentially complex, the importance of clarity of purpose, goals and analytical perspective is great.
  • #37 Use colour to enhance and clarify a design not obscure, ‘shout’ or confuseTo measure/encode/highlight data values
  • #43 Use scatter plots to undertake visual analysis and immerse yourself in your raw material
  • #45 With data so large and potentially complex, the importance of clarity of purpose, goals and analytical perspective is great.
  • #49 Your role is almost that of the movie director, managing all the different streams of tasks and thoughts in order to bring together a cohesive final work
  • #50 Use colour to enhance and clarify a design not obscure, ‘shout’ or confuseTo measure/encode/highlight data values
  • #51 Use colour to enhance and clarify a design not obscure, ‘shout’ or confuseTo measure/encode/highlight data values
  • #52 Whilst serendipitous discovery should be encouraged and accommodated, the parameters surrounding a project mean that tactics, efficiency and focus are paramount Often linked to brief: Are you commissioned to create a specific design to tell a specific story or rather encouraged to find your own important story to tell?
  • #60 Use colour to enhance and clarify a design not obscure, ‘shout’ or confuseTo measure/encode/highlight data values
  • #63 Whereas here the efforts to gather insights are so great that we get very little reward
  • #64 Never use colour/Hue to portray quantitative values
  • #75 Every visual property should serve a purpose
  • #78 Importance of visual annotation to highlight a key insight – as seen by these trend line and reference markers
  • #88 Discussion about tools, when to finish and how to evaluate
  • #89 Books
  • #101 With data so large and potentially complex, the importance of clarity of purpose, goals and analytical perspective is great.
  • #102 You’re also expected to be a super hero with all the abilities perfectly aligned, balanced and deployable at a moment’s notice.  It’s just not that likely, so you will usually rely on a team.  The advice I received from most people was ‘stay close, connected and together as a team’.