Data Visualization. History, present and challanges. Giorgio DensityDesign Research

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1 Università degli Studi di Milano-Bicocca - November 20th 2015 Data Visualization History, present and challanges Giorgio DensityDesign Research

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3 TEAM Prof. Paolo Ciuccarelli Michele Mauri Azzurra Pini Matteo Azzi Daniele Ciminieri Giorgio Uboldi Gabriele Colombo Angeles Briones

4 A Research Lab.

5 designers

6 to visualize data and information

7 to visualize data and information

8 to make complex phenomena (visually) accessible and usable

9 to design (visual) compelling (data) interfaces

10 Why do we visualize?

11 The mind, basically, is a pattern-seeking machine We tend to seek patterns and then we tell stories about them. Stephen Jay Gould

12 David McCandless, 2010

13 People have always tried to visually organize phenomena that weren t readily explicable

14 trying to give a meaning to the data available

15 thanks to the tools they developed

16 Earliest known attempt to show changing values graphically (950 a.c.)

17 conceiving and re-conceiving visual models

18 conceiving and re-conceiving visual models

19 an example Representations of the Earth have undergone many changes throughout the centuries Anaximander ( a.c)

20 an example Sometimes they were a reflection of political or religious influences Heinrich Bünting (1581)

21 an example Sometimes they are a reflection of political or religious influences Mercator Projection (1569)

22 an example Sometimes they are a reflection of political or religious influences Mercator Projection (1569)

23 All kinds of visualization have a shared goal: to make sense of reality

24 John Snow ( ) Florence Nightingale ( )

25 London, 1854

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30 Crimean War, 1854

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32 Crimean War, 1854

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36 Visualization, Graphics, and Statistics 2011, Andrew Gelman and Antony Unwin

37 Visualization, Graphics, and Statistics 2011, Andrew Gelman and Antony Unwin

38 And once policymakers were alerted by Nightingale s dramatic visualization, they were able to scan the columns of numbers directly and understand what was going on. Visualization, Graphics, and Statistics 2011, Andrew Gelman and Antony Unwin

39 The role of the graph was to dramatize the problem and motivate people to go back and look at the numbers. Visualization, Graphics, and Statistics 2011, Andrew Gelman and Antony Unwin

40 Visualization is important

41 Visualization is not enough

42 Great data visualization tells a great story.

43 Great data visualization tells a great story. Great data can produce a great argument. Data can be boring.

44 Great data visualization tells a great story. Great data can produce a great argument. Data can be boring. Great arguments can produce a great story.

45 90% of all the data in the world has been generated over the last two years. SINTEF, 2013

46 IBM, 2012

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49 Data are widely available; what is scarce is the ability to extract wisdom from them. Hal Varian (Google s chief economist), 2010

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52 Information Overload

53 Information Overload Abundance

54 Information Overload Abundance = Transparency Participation Self-empowerment Improved or products and services Innovation Efficiency of government services Effectiveness of government services New knowledge from combined data sources

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56 See?

57 Effective design is not just a matter of making text pretty or entertaining, but of shaping understanding and clarifying meaning Emerson, 2008

58 Design is the intermediary between information and understanding. Grefe R., Executive Director, AIGA

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61 "What is the right way to visualize this information?"

62 It depends

63 1-Who is the user? 2-What is the context? 3-What is the purpose?

64 No rules?

65 Best practices

66 Best practices Jacques Bertin, Sémiologie Graphique (1967)

67 data items visual marks points, lines, areas, surfaces and volumes Jacques Bertin, Sémiologie Graphique (1967)

68 data attributes visual properties of marks position, size, shape, values, colour, orientation texture Jacques Bertin, Sémiologie Graphique (1967)

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71 Data-Ink Ratio Edward Tufte, The Visual Display of Quantitative (1983)

72 Data-Ink Ratio A large share of ink on a graphic should present datainformation, the ink changing as the data change. Dataink is the non-erasable core of a graphic, the nonredundant ink arranged in response to variation in the numbers represented. Edward Tufte, The Visual Display of Quantitative (1983)

73 Data-Ink Ratio Above all else show the data Edward Tufte, The Visual Display of Quantitative (1983)

74 Junk Chart

75 Analysis Narration

76 Formal Approach high formalization low expressive potential strong scientific rigor hard sciences Expressive Approach low formalization high expressive potential weak rigor humanities

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81 Il laboratorio

82 Mercoledì 09 Dicembre 16:30-18:30 Venerdì 11 Dicembre 12:30-14:30 Mercoledì 16 Dicembre 16:30-18:30 Venerdì 15 Gennaio 12:30-14:30 Mercoledì 20 Gennaio 16:30-18:30 Venerdì 22 Gennaio 12:30-14:30 Bring your laptop if possible Suggested softwares: Excel, Illustrator (trial version), TextWrangler, Sublime, Gephi.

83 Visual Bootstrapping aka sketching with data

84 Visual models Tools, examples and suggestions on how to visualize quantities, comparisons, relationships, distributions, compositions

85 Create a visual-data-narration From visual bootstrapping to a web page