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Evaluating the Effect of
Style in Information
Visualization
Andrew Vande Moere 		    KU Leuven, Belgium
Martin Tomitsch 		 	 	   The University of Sydney, Australia
Christoph Wimmer 	 	 	   T.U.Wien, Austria
Christoph Boesch 	 	 	   T.U.Wien, Austria
Thomas Grechenig	 	 	    T.U.Wien, Austria
Goal
•visualizationimpact of style in information
 to measure

 • by comparing 3 different ‘design alternatives’
   • in terms of visual and interactive style
 • style demonstrators based on real-world
   examples
 • then contrasted resulting insights against each
   other
Dataset
•must be ‘agnostic’ to stylistic approach
 • e.g. dance music vs. cancer statistics
•The New York Times news articles
 • containing terms ‘hope’ or ‘fear’ (4,644)
 • title, abstract, date, page number, news desk
 • extra 24 descriptive keywords
“Reversible”
“Factual”
Gapminder (2007)




Many Eyes (2007)




OECD eXplorer (2009)
“Irreversible”
“Meaningful”




    Bitalizer (2008)   Poetry on the Road (2004)   Texone (2005)
Partly “Reversible”
Partly “Factual”

          Digg Swarm (2007)

              ReMap (2009)




                              We Feel Fine (2006)
“Analytical” Style (ANA)
“Magazine” Style (MAG)
“Artistic” Style (ART)
Study
•1. style validation study
 • did our 3 demonstrators correspond to the
   according style examples?
•2. online evaluation study
 • between-subject design
 • recruitment through mailing lists on information
   visualization, HCI, blogs, social media, etc.
Participation


  44
  persons
                             50
                              persons
                                                   44
                                                   persons




                              3.05 level
                         average expertise




            Analytical                  Magazine             Artistic
Interaction

 18m09s
  (average)
                        12m49s
                         (average)
                                                   11m55s
                                                    (average)




   181.0
  interactions
                           87.7
                         interactions
                                                      88.9
                                                    interactions
   (average)              (average)                  (average)




           Analytical                   Magazine                   Artistic
No Reported Insights


     1
  participant
                         11
                        participants
                                                 9
                                              participants




           Analytical              Magazine                  Artistic
Interface “Insights’


  6%
  6 insights
                        12%
                        13 insights
                                                 29%
                                                 27 insights




           Analytical                 Magazine                 Artistic
Insight Typology
         •insight classification (Chen et al.*)
          • 2 independent coders (34% agreement)
          • revisited classification (89% agreement)
          • then decided together (100% agreement)
         •“meaning”: added new insight class
          • all connotations to ‘content’



  (*) 
Y. Chen, J. Yang and W. Ribarsky, “Toward Effective Insight Management in Visual Analytics Systems,” IEEE

      Pacific Visualization Symposium (PacificVis'09), IEEE, 2009, pp. 49-56.
Insight Analysis
                 ANA       MAG         ART
 Difference    24% (24)   26% (26)   17% (11)
     Cluster   22% (22)   15% (15)     9% (6)
Distribution   11% (11)   12% (12)   17% (11)
Compound         9% (9)   14% (14)    11% (7)
       Trend     8% (8)    4% (4)     8% (5)
    Outliers     6% (6)   10% (10)   15% (10)
       Value     6% (6)     1% (1)    0% (0)
Association      5% (5)    3% (3)     6% (4)
Meaning (*)      3% (3)    4% (4)     14% (9)
   Extreme       4% (4)    6% (6)     0% (0)
Categories       2% (2)    1% (1)     0% (0)
       Rank      1% (1)    2% (2)     3% (2)
Insight Analysis
                 ANA       MAG         ART
 Difference    24% (24)   26% (26)   17% (11)
     Cluster   22% (22)   15% (15)     9% (6)
Distribution   11% (11)   12% (12)   17% (11)
Compound         9% (9)   14% (14)    11% (7)
       Trend     8% (8)    4% (4)     8% (5)
    Outliers     6% (6)   10% (10)   15% (10)
       Value     6% (6)     1% (1)    0% (0)
Association      5% (5)    3% (3)     6% (4)
Meaning (*)      3% (3)    4% (4)     14% (9)
   Extreme       4% (4)    6% (6)     0% (0)
Categories       2% (2)    1% (1)     0% (0)
       Rank      1% (1)    2% (2)     3% (2)
Insight Analysis
    Rating (1 - 5)          ANA          MAG            ART
 uncertain - confident   4.10 (1.11)   4.21 (0.87)   4.17 (0.95)
Insight Analysis
    Rating (1 - 5)           ANA          MAG            ART
 uncertain - confident    4.10 (1.11)   4.21 (0.87)   4.17 (0.95)

       difficult - easy   3.78 (1.17)   3.63 (1.29)   4.00 (1.24)
Insight Analysis
    Rating (1 - 5)           ANA          MAG            ART
 uncertain - confident    4.10 (1.11)   4.21 (0.87)   4.17 (0.95)

       difficult - easy   3.78 (1.17)   3.63 (1.29)   4.00 (1.24)

      shallow - deep     3.18 (1.10)   2.93 (1.08)   2.54 (1.17)
Insight Analysis
            Rating (1 - 5)           ANA          MAG            ART
        uncertain - confident     4.10 (1.11)   4.21 (0.87)   4.17 (0.95)

               difficult - easy   3.78 (1.17)   3.63 (1.29)   4.00 (1.24)

               shallow - deep    3.18 (1.10)   2.93 (1.08)   2.54 (1.17)

shallow – deep (expert rating)   2.44 (0.78)   2.36 (0.70)   2.28 (0.64)
Insight Analysis
 Rating (1 - 5)           ANA          MAG            ART
   ugly - beautiful   3.48 (0.85)   3.08 (1.03)   3.11 (1.02)
  obtrusive - fluid    3.27 (0.95)   3.08 (1.01)   2.80 (1.00)
Insight Analysis
          Rating (1 - 5)             ANA          MAG            ART
             ugly - beautiful    3.48 (0.85)   3.08 (1.03)   3.11 (1.02)
             obtrusive - fluid    3.27 (0.95)   3.08 (1.01)   2.80 (1.00)
          ambiguous - clear      3.39 (1.17)   1.98 (0.89)   2.00 (0.86)
difficult - easy to understand    3.55 (1.04)   2.08 (1.07)   2.14 (1.07)
  intended inform – express      2.80 (1.15)   3.54 (1.18)   3.66 (1.06)
             useless - useful    3.61 (0.95)   2.70 (1.09)   2.45 (0.90)
      frustrating - enjoyable    3.43 (1.00)   2.54 (1.16)   2.34 (1.06)
          unusable - usable      3.77 (0.91)   2.78 (1.13)   2.64 (1.12)
           boring - engaging     3.43 (0.93)   3.10 (0.95)   2.80 (1.00)
  non-functional - functional    3.93 (0.82)   2.80 (1.18)   2.50 (1.13)
                    tool - art   2.30 (1.07)   3.32 (1.19)   3.68 (0.93)
Discussion
•insight classification
 • based on very short descriptions (M=17.86)
 • methodology missing to benchmark insights
   against each other
•‘controlling’ style
 • are the 3 conditions representative?
   • e.g. similarities MAG / ART
Conclusions
•style impacts perception of usability
 • in particular for embellished versus non-
   embellished styles
 • analytical style was perceived as more
   understandable, clear, enjoyable, engaging,
   useful, functional, ...
Conclusions
•style does not impact insight depth
 • participants were able to overcome huge
   incomprehensibility issues of ART
 • and in a minimum amount of time
Conclusions
•style has impact on ‘kind’ of insights
 • analytical focus of facts versus meaning of
   content, explanation of reasoning, ...
 • driven by e.g. graphic incorporation of content,
   fluidity of interface, ...
Guidelines
•to accurately benchmark insights...
 • make distinction between analytical
   characteristics of an insight and its meaning
 • motivate participants to report insights in a more
   expansive way
   • e.g. insight categorization,...
 • allow participants to report usability issues in
   parallel with insights
 • consider alternative ways of insight analysis
   • e.g. card sorting, affinity diagramming,...
Thank you!
Andrew Vande Moere
andrew.vandemoere@asro.kuleuven.be
@infosthetics
Introduction Stage




                     continue >
Insight Recording Stage
Survey Stage

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Evaluating the Effect of Style in Information Visualization

  • 1. Evaluating the Effect of Style in Information Visualization Andrew Vande Moere KU Leuven, Belgium Martin Tomitsch The University of Sydney, Australia Christoph Wimmer T.U.Wien, Austria Christoph Boesch T.U.Wien, Austria Thomas Grechenig T.U.Wien, Austria
  • 2. Goal •visualizationimpact of style in information to measure • by comparing 3 different ‘design alternatives’ • in terms of visual and interactive style • style demonstrators based on real-world examples • then contrasted resulting insights against each other
  • 3. Dataset •must be ‘agnostic’ to stylistic approach • e.g. dance music vs. cancer statistics •The New York Times news articles • containing terms ‘hope’ or ‘fear’ (4,644) • title, abstract, date, page number, news desk • extra 24 descriptive keywords
  • 5. “Irreversible” “Meaningful” Bitalizer (2008) Poetry on the Road (2004) Texone (2005)
  • 6. Partly “Reversible” Partly “Factual” Digg Swarm (2007) ReMap (2009) We Feel Fine (2006)
  • 10. Study •1. style validation study • did our 3 demonstrators correspond to the according style examples? •2. online evaluation study • between-subject design • recruitment through mailing lists on information visualization, HCI, blogs, social media, etc.
  • 11. Participation 44 persons 50 persons 44 persons 3.05 level average expertise Analytical Magazine Artistic
  • 12. Interaction 18m09s (average) 12m49s (average) 11m55s (average) 181.0 interactions 87.7 interactions 88.9 interactions (average) (average) (average) Analytical Magazine Artistic
  • 13. No Reported Insights 1 participant 11 participants 9 participants Analytical Magazine Artistic
  • 14. Interface “Insights’ 6% 6 insights 12% 13 insights 29% 27 insights Analytical Magazine Artistic
  • 15. Insight Typology •insight classification (Chen et al.*) • 2 independent coders (34% agreement) • revisited classification (89% agreement) • then decided together (100% agreement) •“meaning”: added new insight class • all connotations to ‘content’ (*) Y. Chen, J. Yang and W. Ribarsky, “Toward Effective Insight Management in Visual Analytics Systems,” IEEE Pacific Visualization Symposium (PacificVis'09), IEEE, 2009, pp. 49-56.
  • 16. Insight Analysis ANA MAG ART Difference 24% (24) 26% (26) 17% (11) Cluster 22% (22) 15% (15) 9% (6) Distribution 11% (11) 12% (12) 17% (11) Compound 9% (9) 14% (14) 11% (7) Trend 8% (8) 4% (4) 8% (5) Outliers 6% (6) 10% (10) 15% (10) Value 6% (6) 1% (1) 0% (0) Association 5% (5) 3% (3) 6% (4) Meaning (*) 3% (3) 4% (4) 14% (9) Extreme 4% (4) 6% (6) 0% (0) Categories 2% (2) 1% (1) 0% (0) Rank 1% (1) 2% (2) 3% (2)
  • 17. Insight Analysis ANA MAG ART Difference 24% (24) 26% (26) 17% (11) Cluster 22% (22) 15% (15) 9% (6) Distribution 11% (11) 12% (12) 17% (11) Compound 9% (9) 14% (14) 11% (7) Trend 8% (8) 4% (4) 8% (5) Outliers 6% (6) 10% (10) 15% (10) Value 6% (6) 1% (1) 0% (0) Association 5% (5) 3% (3) 6% (4) Meaning (*) 3% (3) 4% (4) 14% (9) Extreme 4% (4) 6% (6) 0% (0) Categories 2% (2) 1% (1) 0% (0) Rank 1% (1) 2% (2) 3% (2)
  • 18. Insight Analysis Rating (1 - 5) ANA MAG ART uncertain - confident 4.10 (1.11) 4.21 (0.87) 4.17 (0.95)
  • 19. Insight Analysis Rating (1 - 5) ANA MAG ART uncertain - confident 4.10 (1.11) 4.21 (0.87) 4.17 (0.95) difficult - easy 3.78 (1.17) 3.63 (1.29) 4.00 (1.24)
  • 20. Insight Analysis Rating (1 - 5) ANA MAG ART uncertain - confident 4.10 (1.11) 4.21 (0.87) 4.17 (0.95) difficult - easy 3.78 (1.17) 3.63 (1.29) 4.00 (1.24) shallow - deep 3.18 (1.10) 2.93 (1.08) 2.54 (1.17)
  • 21. Insight Analysis Rating (1 - 5) ANA MAG ART uncertain - confident 4.10 (1.11) 4.21 (0.87) 4.17 (0.95) difficult - easy 3.78 (1.17) 3.63 (1.29) 4.00 (1.24) shallow - deep 3.18 (1.10) 2.93 (1.08) 2.54 (1.17) shallow – deep (expert rating) 2.44 (0.78) 2.36 (0.70) 2.28 (0.64)
  • 22. Insight Analysis Rating (1 - 5) ANA MAG ART ugly - beautiful 3.48 (0.85) 3.08 (1.03) 3.11 (1.02) obtrusive - fluid 3.27 (0.95) 3.08 (1.01) 2.80 (1.00)
  • 23. Insight Analysis Rating (1 - 5) ANA MAG ART ugly - beautiful 3.48 (0.85) 3.08 (1.03) 3.11 (1.02) obtrusive - fluid 3.27 (0.95) 3.08 (1.01) 2.80 (1.00) ambiguous - clear 3.39 (1.17) 1.98 (0.89) 2.00 (0.86) difficult - easy to understand 3.55 (1.04) 2.08 (1.07) 2.14 (1.07) intended inform – express 2.80 (1.15) 3.54 (1.18) 3.66 (1.06) useless - useful 3.61 (0.95) 2.70 (1.09) 2.45 (0.90) frustrating - enjoyable 3.43 (1.00) 2.54 (1.16) 2.34 (1.06) unusable - usable 3.77 (0.91) 2.78 (1.13) 2.64 (1.12) boring - engaging 3.43 (0.93) 3.10 (0.95) 2.80 (1.00) non-functional - functional 3.93 (0.82) 2.80 (1.18) 2.50 (1.13) tool - art 2.30 (1.07) 3.32 (1.19) 3.68 (0.93)
  • 24. Discussion •insight classification • based on very short descriptions (M=17.86) • methodology missing to benchmark insights against each other •‘controlling’ style • are the 3 conditions representative? • e.g. similarities MAG / ART
  • 25. Conclusions •style impacts perception of usability • in particular for embellished versus non- embellished styles • analytical style was perceived as more understandable, clear, enjoyable, engaging, useful, functional, ...
  • 26. Conclusions •style does not impact insight depth • participants were able to overcome huge incomprehensibility issues of ART • and in a minimum amount of time
  • 27. Conclusions •style has impact on ‘kind’ of insights • analytical focus of facts versus meaning of content, explanation of reasoning, ... • driven by e.g. graphic incorporation of content, fluidity of interface, ...
  • 28. Guidelines •to accurately benchmark insights... • make distinction between analytical characteristics of an insight and its meaning • motivate participants to report insights in a more expansive way • e.g. insight categorization,... • allow participants to report usability issues in parallel with insights • consider alternative ways of insight analysis • e.g. card sorting, affinity diagramming,...
  • 29. Thank you! Andrew Vande Moere andrew.vandemoere@asro.kuleuven.be @infosthetics
  • 30. Introduction Stage continue >