### TorCHI.org World Usability Day 2018 - Talk by Prof. Fanny Chevalier

1. HOW TO CULTIVATE CRITICAL THINKING THROUGH VISUALIZATION Seeing beyond the graph: Fanny Chevalier Assistant professor University of Toronto
2. Winnpeg Sun, 2013The West Morland Gazette, 2015unknown university board
3. Map source: www.npr.org
4. Image source: REUTERS/Carlos Barria
5. Source: John Muyskens,Washington post, October 2016
6. Source: John Muyskens,Washington post, October 2016
7. How well do we understand data that we see everyday? Relate: Using concrete scales to communicate complex measures How well do we understand data this represented visually? Educate:Visualization literacy at elementary school How well can we communicate data visually? Engage:Visualization literacy at elementary school 1. 2. 3.
8. How well do we understand the data that we see everyday? 1.
9. 350 ml 475 ml 500 ml 39g of sugar 52g of sugar 65g of sugar Recommended daily intake: 25g (World Health Organization)
10. PRESENTATIONS 350 ml 475 ml 500 ml 39g of sugar 52g of sugar 65g of sugar Recommended daily intake: 25g (World Health Organization)
11. Image source: REUTERS/Shannon Stapleton
12. Video source: http://www.debtclock.ca/
13. USD\$20.7 trillion USD\$ 20,760,852,675,267 USD\$515 billion CAD\$ 650,881,545,273 USD\$ 512,237,267,314 Retrieved on Feb.26, 10:45AM EST from: http://www.usdebtclock.org/ and http://www.debtclock.ca/
14. A concrete scale is a visualization that conveys a complex measure by re-expressing it using familiar objects from the real world 20 trillions of dollars59g of sugar Using Concrete Scales:A Framework forVisual Depiction of Complex Measures Chevalier, Vuillemot & Gali. IEEETVCG / Infovis 2013
15. COGNITIVE MECHANISMS Anchor and adjustment Unitization Analogy 0 measure 0 measure 0 measure Using Concrete Scales:A Framework forVisual Depiction of Complex Measures Chevalier, Vuillemot & Gali. IEEETVCG / Infovis 2013
16. How well do we understand the data that we see everyday? 1. Some measures may be difﬁcult to grasp. Relate data to more familiar concepts can help.
17. How well do we understand data that is represented visually? 2.
19. Perceptual biases
20. Image source: NPR visual team.
21. VISUALIZATION LITERACY ability to extract information from visualizations and think critically about it ability to communicated data in a visual form as truthfully as possible
22. Where and when do we learn these skills?
23. Qualitative coding of textbook visual material C’est la vis:Visualization Literacy at Elementary School Alper, Henry Riche, Chevalier, Boy & Sezgin.ACM CHI 2017.
24. We encoded 2600 visual representations in 5000 textbook pages C’est la vis:Visualization Literacy at Elementary School Alper, Henry Riche, Chevalier, Boy & Sezgin.ACM CHI 2017.
25. Degree of Abstraction Scott McCloud, Understanding Comics Degree of Abstraction Scott McCloud, Understanding Comics C’est la vis:Visualization Literacy at Elementary School Alper, Henry Riche, Chevalier, Boy & Sezgin.ACM CHI 2017.
26. C’est la vis:Visualization Literacy at Elementary School Alper, Henry Riche, Chevalier, Boy & Sezgin.ACM CHI 2017. Same data, different abstraction levels
27. C’est la vis:Visualization Literacy at Elementary School Alper, Henry Riche, Chevalier, Boy & Sezgin.ACM CHI 2017.
28. Interested and focused for 30 minutes Disrupted class dynamics Interacted a lot Completed all activities Peer learning Verbalization Retention
29. How well do we understand data that is represented visually? 2. Decoding and understanding visualizations is a complex activity that requires knowledge and skill. Visualization education can help.
30. How well can we communicate data visually? 3.
31. VISUALIZATION LITERACY ability to extract information from visualizations and think critically about it ability to communicated data in a visual form as truthfully as possible
32. http://coloringclimate.com/
33. Color where carbon emissions from fossil fuels are the greatest, which looks similar to a population density map since this type of greenhouse gas is caused by human activity. http://coloringclimate.com/ See if you can color 20 football ﬁelds in under a minute, which is about how fast we have been losing global forests for the past 25 years
34. http://www.dear-data.com/
35. http://www.dear-data.com/
36. DataInk: Creative and Direct Data-Oriented Drawing Xia, Riche, Chevalier, De Araujo,Wigdor.ACM CHI 2018 [Honourable mention]
37. DataInk: Creative and Direct Data-Oriented Drawing Xia, Riche, Chevalier, De Araujo,Wigdor.ACM CHI 2018. [Honourable mention]
38. How well can we communicate data visually? 3. There are many challenges. By reconciling creative drawing and data binding we can engage people with data.
39. –Alberto Cairo “[data is] always noisy, dirty, and uncertain.”
40. –Fanny Chevalier “… and so is the way we portray and perceive it.”
41. Relate Educate Engage 1. 2. 3. http://fannychevalier.net