Data Visualization Tools & Techniques
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1 Data Visualization Tools & Techniques John Brosz Research Data & Visualization Coordinator Renée Reaume Digital Media & Technical Services Director
2 Plan for Today 9:00 Intro 9:30 Visual Variables 10:15 Break 10:30 Sketching 11:30 Vis Software & Tools 12:00 Lunch 1:00 Tableau 2:30 Break 3:30 Vis in the Library & Professional Practice 4:00 Work with Data
3 Icebreaker Introduce yourself Do you have a visualization project? What s your goal for the day?
4 What is Data Visualization?
5 What is Visualization? Visual representation of data Transformation of the symbolic into the geometric [McCormick et al, 1987] artificial memory that best supports our natural means of perception [Bertin, 1967] Use of computer-generated, interactive, visual representations of data to amplify cognition [Card, Mackinlay, & Shneidermann, 1999]
6 What is Visualization? 1. Based on non-visual data 2. Produce an image 3. Result must be readable and recognizable Robert Kosara
7 Scientific Visualization & Information Visualization commons/5/54/rayleigh- Taylor_instability.jpg
8 InfoGraphics vs Visualization A visualization can be applied to many datasets; an infographic is created for a particular dataset.
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10 Why do we Visualize? The ability to take data to be able to understand it, to visualize it, to communicate it that s going to be a hugely important skill in the next decades, because now we really do have essentially free and ubiquitous data. So the complimentary scarce factor is the ability to understand the data and extract value from it. Hal Varian, Google s Chief Economist The McKinsey Quarterly, Jan 2009
11 Why do we Visualize? Visual bandwidth is enormous
12 Why do we Visualize? Visual bandwidth is enormous Human perceptual skills are remarkable Trend, cluster, gap, outlier, pattern... Color, size, shape, proximity...
13 Perception How many 3s?
14 Perception How many 3s?
15 How many s? Perception
16 How many s? Perception
17 Set A X Y Set B X Y Set C X Y Set D X Y Anscombe s Quartet (Anscombe, Francis J., 1973)
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19 Why do we Visualize? Visual bandwidth is enormous Human perceptual skills are remarkable Trend, cluster, gap, outlier, pattern... Color, size, shape, proximity... External representation Reduces load on working memory Offload cognition
20 External Representation Paper Mental Buffer 57 X 48
21 External Representation Paper Mental Buffer 57 X 48 7*8 = 56
22 External Representation Paper Mental Buffer 5 57 X 48 7*8 = 56 6
23 External Representation Paper Mental Buffer 5 57 X 48 6
24 External Representation Paper Mental Buffer 5 57 X 48 5*8 = =
25 External Representation Paper Mental Buffer 5 57 X
26 External Representation Paper Mental Buffer X 48 4 * 7 =
27 External Representation Paper Mental Buffer X
28 External Representation Paper Mental Buffer X 48 4 * 5 = =
29 External Representation Paper Mental Buffer X
30 External Representation Paper Mental Buffer 57 X = = = = = 2
31 External Representation Paper Mental Buffer 57 X = = = = = 2
32 External Representation: Example Paradoxes Lewis Carroll Paradoxes Epimenides Paradoxes Self-ref Paradoxes Infinity Epimenides Self-ref Epimenides Tarski Halting Problem Decision Procedure Halting Problem - Turing Infinity Halting problem Infinity Recursion Infinity Zeno Infinity Lewis Carroll Zeno Lewis Carroll Lewis Carroll Wordplay Tarski Epimenides Tarski Truth vs Provability Tarski - Undecidability
33 External Representation
34 Why do we Visualize? Visual bandwidth is enormous Human perceptual skills are remarkable Trend, cluster, gap, outlier, pattern... Color, size, shape, proximity... External representation Reduces load on working memory Offload cognition Fewer coded symbols to decode
35 Decoding Compare: 2947 vs 6621 vs 95.12
36 Decoding Compare: 2947 vs 6621 vs
37 Why Don t We Visualize Myths: We visualize data because some people are visual learners. We visualize data for people who have difficulty understanding numbers. We visualize data to grab people s attention with eyecatching displays. Stephen Few
38 Why Visualize in the Library? Your Practice Explore our data
39 Why Visualize in the Library? Your Practice Explore our data Communicate our data
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41 Why Visualize in the Library? Your Practice Explore our data Communicate our data 21 st Century Competency External Practice Support data resources Tool across entire academy
42 Visualization Examples Map of the successive losses in men of the French Army in the Russian campaign Charles Minard,
43 John Snow s Cholera Map, London
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47 Gapminder
48 Exercise: How many ways can you communicate two quantities? 75 and 37
49 Exercise: How many ways can you communicate two quantities? 75 and 37
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52 Visualization Resources Books Semiology of Graphics by Jacques Bertin The book on visual variables Visual Display of Quantitative Information, Beautiful Evidence, Visual Explanations, or Envisioning Information by Edward Tufte Beautiful examples of historic visualizations Visualizing Data by Ben Fry Current overarching text book Visual Thinking for Design by Colin Ware Ties perception theory and design processes to visualization practices. Beautiful Visualization by Steele & Iliinsky Combines techniques from artists, designers, scientists, and others.
53 Visualization Resources Web Sites New York Times Dedicated team producing exceptional work. Information aesthetics Creative design applied to visualization. Gorgeous. Eagereyes Vis Researcher with criticism as well as overview from assorted research conferences. Gapminder Hans Rosling s stat software & data. Visual Business Intelligence Analytics blog, frequently discussing big data and dashboard design. Tableau Viz of the Day Daily example of a visualization created with Tableau software.
54 Plan for Today 9:00 Intro 9:30 Visual Variables 10:15 Break 10:30 Sketching 11:30 Vis Software & Tools 12:00 Lunch 1:00 Tableau 2:30 Break 3:30 Vis in the Library & Professional Practice 4:00 Work with Data
55 Creating a Visualization 1. Know the Data 2. Know the Audience/Purpose/Venue 3. Visually Encode the Data
56 1 - Know the Data Number of attributes Examples, Weather: date/time, temperature, precipitation, humidity, wind, forecast, pressure, cloud cover. Real Estate: price, location, square feet, lot size, floors, # bathrooms, # bedrooms, etc. Quality of the data Missing, uncertain, bad values Amount of data Data Types
57 Data Types Categorical Ordered Ordinal Quantitative Small Medium Large Spatial
58 2 Know the Audience Who is the for? What is your goal? What is interesting? Story? Interactive? Display capacity?
59 3 Visually Encode Requires awareness of: Human perceptual system Display capacity Characteristics of data (size, type) Task Step #2
60 Marks 3 Visually Encode
61 3 Visually Encode Marks What can we change about a mark?
62 Visual Channels: Visual Encoding Size Position Angle Slope Length Area Volume Shape Independent? Lightness Saturation Hue Texture Connection Containment Colour? Opacity Sketchiness
63 Visual Encoding How well can we judge a channel s magnitude Are differences perceivable? How many bins? Ideally all channels would be independent (not so) Interactions between channels
64 Visual Variable Properties 1. Selective Is a change of a mark in this variable alone enough to allow us to select it from other marks? 2. Associative Can we identify a group of marks in this variable? 3. Quantitative Can the relation between two of these marks be seen as numeric? Can we tell if one is 3X another? 4. Order Does this variable support ordered reading (more/less)? 5. Length How many differences in this variable can be discerned?
65 Visual Variable Properties 1. Selective Is a change of a mark in this variable alone enough to allow us to select it from other marks? 2. Associative Can we identify a group of marks in this variable? 3. Quantitative Can the relation between two of these marks be seen as numeric? Can we tell if one is 3X another? 4. Order Does this variable support ordered reading (more/less)? 5. Length How many differences in this variable can be discerned?
66 Is Size Selective? Can you find the big & small Muppets? Muppet drawings from
67 Is Size Selective? Can you find the big & small Muppets? Muppet drawings from
68 Visual Variable Properties 1. Selective Is a change of a mark in this variable alone enough to allow us to select it from other marks? 2. Associative Can we identify a group of marks by this variable? 3. Quantitative Can the relation between two of these marks be seen as numeric? Can we tell if one is 3X another? 4. Order Does this variable support ordered reading (more/less)? 5. Length How many differences in this variable can be discerned?
69 Is Size Associative? Can you find the small Muppets? Muppet drawings from
70 Is Size Associative? Can you find the small Muppets? Muppet drawings from
71 Visual Variable Properties 1. Selective Is a change of a mark in this variable alone enough to allow us to select it from other marks? 2. Associative Can we identify a group of marks in this variable? 3. Quantitative Can the relation between two of these marks be seen as numeric? Can we tell if one is 3X another? 4. Order Does this variable support ordered reading (more/less)? 5. Length How many differences in this variable can be discerned?
72 Is Size Quantitative? What value is Kermit compared to Fozzie? Muppet drawings from
73 Is Size Quantitative? What value is Kermit compared to Fozzy? Muppet drawings from
74 Is Size Quantitative? What value is Kermit compared to? = =
75 Visual Variable Properties 1. Selective Is a change of a mark in this variable alone enough to allow us to select it from other marks? 2. Associative Can we identify a group of marks in this variable? 3. Quantitative Can the relation between two of these marks be seen as numeric? Can we tell if one is 3X another? 4. Order Does this variable support ordered reading (more/less)? 5. Length How many differences in this variable can be discerned?
76 Is Size Ordered? Muppet drawings from
77 Visual Variable Properties 1. Selective Is a change of a mark in this variable alone enough to allow us to select it from other marks? 2. Associative Can we identify a group of marks in this variable? 3. Quantitative Can the relation between two of these marks be seen as numeric? Can we tell if one is 3X another? 4. Order Does this variable support ordered reading (more/less)? 5. Length How many differences in this variable can be discerned?
78 What Length Does Size Have? Muppet drawings from
79 What Length Does Size Have? Association/Selection: ~5 Distinction: ~20 Muppet drawings from
80 Visual Encoding Channel Properties Selective Is a change in this variable alone enough to allow us to select it from a group? Associative Can we identify a group of these marks? Quantitative Can the relation between two of these marks be seen as numeric? Can we tell if one is 3X as much as another? Order Does this variable support ordered reading (more/less)? Length How many differences in this variable can be discerned?
81 Size Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Shape Lightness Saturation Hue Angle Texture
82 Position Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Shape Lightness Saturation Hue Angle Texture
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84 Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Shape Lightness Saturation Hue Angle Texture
85 Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Shape Lightness Saturation Hue Angle Texture
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87 Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Shape Lightness Saturation Hue Angle Texture
88 Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Shape Lightness Saturation Hue Angle Texture
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90 Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Shape Lightness Saturation Hue Angle Texture
91 Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Shape Lightness Saturation Hue Angle Texture
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93 Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Shape Lightness Saturation Hue Angle Texture
94 Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Shape Lightness Saturation Hue Angle Texture
95 Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Infinite Shape Lightness Saturation Hue Angle Texture Theoretically infinite but dependent on display size
96 Shape Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Infinite Shape Lightness Saturation Hue Angle Texture
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98 Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Infinite Shape Yes Lightness Saturation Hue Angle Texture
99 Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Infinite Shape Yes Lightness Saturation Hue Angle Texture
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101 Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Infinite Shape Yes Yes Lightness Saturation Hue Angle Texture
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103 Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Infinite Shape < 5 < 5 Lightness Saturation Hue Angle Texture
104 Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Infinite Shape < 5 < 5 Lightness Saturation Hue Angle Texture
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106 Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Infinite Shape < 5 < 5 No No Lightness Saturation Hue Angle Texture
107 Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Infinite Shape < 5 < 5 No No 5 / Infinite Lightness Saturation Hue Angle Texture Theoretically infinite
108 Lightness Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Infinite Shape < 5 < 5 No No 5 / Infinite Lightness Saturation Hue Angle Texture
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110 Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Infinite Shape < 5 < 5 No No 5 / Infinite Lightness Yes Saturation Hue Angle Texture
111 Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Infinite Shape < 5 < 5 No No 5 / Infinite Lightness Yes Saturation Hue Angle Texture
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113 Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Infinite Shape < 5 < 5 No No 5 / Infinite Lightness Yes Yes Saturation Hue Angle Texture
114 Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Infinite Shape < 5 < 5 No No 5 / Infinite Lightness Yes Yes Saturation Hue Angle Texture
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116 Weber s Law: human perception is fundamentally based on relative judgments, not absolute values.
117 Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Infinite Shape < 5 < 5 No No 5 / Infinite Lightness Yes Yes No Saturation Hue Angle Texture
118 Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Infinite Shape < 5 < 5 No No 5 / Infinite Lightness Yes Yes No Saturation Hue Angle Texture
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120 Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Infinite Shape < 5 < 5 No No 5 / Infinite Lightness Yes Yes No Yes Saturation Hue Angle Texture
121 Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Infinite Shape < 5 < 5 No No 5 / Infinite Lightness Yes Yes No Yes 7 / 10 Saturation Hue Angle Texture Theoretically infinite
122 Saturation (same as Lightness) Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Infinite Shape < 5 < 5 No No 5 / Infinite Lightness Yes Yes No Yes 7 / 10 Saturation Yes Yes No Yes 7 / 10 Hue Angle Texture
123 Hue Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Infinite Shape < 5 < 5 No No 5 / Infinite Lightness Yes Yes No Yes 7 / 10 Saturation Yes Yes No Yes 7 / 10 Hue Angle Texture
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125 Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Infinite Shape < 5 < 5 No No 5 / Infinite Lightness Yes Yes No Yes 7 / 10 Saturation Yes Yes No Yes 7 / 10 Hue Yes Angle Texture
126 Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Infinite Shape < 5 < 5 No No 5 / Infinite Lightness Yes Yes No Yes 7 / 10 Saturation Yes Yes No Yes 7 / 10 Hue Yes Angle Texture
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128 Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Infinite Shape < 5 < 5 No No 5 / Infinite Lightness Yes Yes No Yes 7 / 10 Saturation Yes Yes No Yes 7 / 10 Hue Yes Yes Angle Texture
129 Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Infinite Shape < 5 < 5 No No 5 / Infinite Lightness Yes Yes No Yes 7 / 10 Saturation Yes Yes No Yes 7 / 10 Hue Yes Yes Angle Texture
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135 Destroys Detail Image from Borland & Taylor II (2007), Rainbow Color Map (Still) Considered Harmful. IEEE Visualization Viewpoints, pp
136 Rainbow Gray Black-Body Radiation Red-Green Image from Borland & Taylor II (2007), Rainbow Color Map (Still) Considered Harmful. IEEE Visualization Viewpoints, pp
137 Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Infinite Shape < 5 < 5 No No 5 / Infinite Lightness Yes Yes No Yes 7 / 10 Saturation Yes Yes No Yes 7 / 10 Hue Yes Yes Not advisable Not advisable Angle Texture
138 Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Infinite Shape < 5 < 5 No No 5 / Infinite Lightness Yes Yes No Yes 7 / 10 Saturation Yes Yes No Yes 7 / 10 Hue Yes Yes Not advisable Not advisable 7 / 10 Angle Texture
139 Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Infinite Shape < 5 < 5 No No 5 / Infinite Lightness Yes Yes No Yes 7 / 10 Saturation Yes Yes No Yes 7 / 10 Hue Yes Yes Not advisable Not advisable 7 / 10 Angle Texture
140 Angle Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Infinite Shape < 5 < 5 No No 5 / Infinite Lightness Yes Yes No Yes 7 / 10 Saturation Yes Yes No Yes 7 / 10 Hue Yes Yes Not advisable Not advisable 7 / 10 Angle Texture
141 Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Infinite Shape < 5 < 5 No No 5 / Infinite Lightness Yes Yes No Yes 7 / 10 Saturation Yes Yes No Yes 7 / 10 Hue Yes Yes Not advisable Not advisable 7 / 10 Angle Yes Yes No No 4 / 8 Texture
142 Texture (mostly same as Hue) Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Infinite Shape < 5 < 5 No No 5 / Infinite Lightness Yes Yes No Yes 7 / 10 Saturation Yes Yes No Yes 7 / 10 Hue Yes Yes Not advisable Not advisable 7 / 10 Angle Yes Yes No No 4 / 8 Texture
143 Angle (mostly same as Hue) Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Infinite Shape < 5 < 5 No No 5 / Infinite Lightness Yes Yes No Yes 7 / 10 Saturation Yes Yes No Yes 7 / 10 Hue Yes Yes Not advisable Not advisable 7 / 10 Angle Yes Yes No No 4 / 8 Texture Yes Yes No No Infinite M.S.T. Carpendale. Considering Visual Variables as a Basis for Information Visualisation. Research report , Department of Computer science, University of Calgary, 2003.
144 Plan for Today 9:00 Intro 9:30 Visual Variables 10:15 Break 10:30 Sketching 11:30 Vis Software & Tools 12:00 Lunch 1:00 Tableau 2:30 Break 3:30 Vis in the Library & Professional Practice 4:00 Work with Data
145 Sketching Exercise Create a visualization Anything you want What interests you in the data? What questions do you have? Challenge: Try to encode as many properties as possible in your visualization.
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151 Variable Selective Associative Quantitative Order Length Size Yes Yes Yes (1D), Mostly Yes 5/20 (2D), Not likely (3D) Position Yes Yes Yes Yes Infinite Shape < 5 < 5 No No 5 / Infinite Lightness Yes Yes No Yes 7 / 10 Saturation Yes Yes No Yes 7 / 10 Hue Yes Yes Not advisable Not advisable 7 / 10 Angle Yes Yes No No 4 / 8 Texture Yes Yes No No Infinite
152 Closing Thought on Visual Variables Why is this useful? Any software tool will use these variables Exercise useful even if design not implemented Understanding Quality Checking/Assurance
153 Plan for Today 9:00 Intro 9:30 Visual Variables 10:15 Break 10:30 Sketching 11:30 Vis Software & Tools 12:00 Lunch 1:00 Tableau 2:30 Break 3:30 Vis in the Library & Professional Practice 4:00 Work with Data
Information Visualization
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