The document appears to be a presentation on information visualization. It discusses definitions of information visualization, examples of early information visualizations throughout history, and potential applications like visualizing publication networks and student activity data. It also covers best practices for visualization like ensuring visuals are designed to be easily interpreted and don't mislead, using proper scaling and layouts, and considering the cognitive strengths and limitations of human perception when designing visualizations.
On National Teacher Day, meet the 2024-25 Kenan Fellows
intro to information visualization
1. HUMAN COMPUTER INTERACTION LAB
INFORMATION
VISUALISATION
capita selecta 17/10/2012
Joris Klerkx
@jkofmsk
Wednesday 17 October 12
2. Imagine you never saw a car...
Would the following definitions help to explain it?
http://www.thefreedictionary.com/car
Wednesday 17 October 12
3. Imagine you never saw a car...
Would the following definitions help to explain it?
http://www.thefreedictionary.com/car
1. It’s an automobile
Wednesday 17 October 12
4. Imagine you never saw a car...
Would the following definitions help to explain it?
http://www.thefreedictionary.com/car
1. It’s an automobile
A phone that automatically takes a call..
Wednesday 17 October 12
5. Imagine you never saw a car...
Would the following definitions help to explain it?
http://www.thefreedictionary.com/car
1. It’s an automobile
A phone that automatically takes a call..
2. It’s a vehicle, such as a streetcar
Wednesday 17 October 12
6. Imagine you never saw a car...
Would the following definitions help to explain it?
http://www.thefreedictionary.com/car
1. It’s an automobile
A phone that automatically takes a call..
2. It’s a vehicle, such as a streetcar
Wednesday 17 October 12
7. Imagine you never saw a car...
Would the following definitions help to explain it?
http://www.thefreedictionary.com/car
1. It’s an automobile
A phone that automatically takes a call..
2. It’s a vehicle, such as a streetcar
3. It’s a boxlike enclosure for passengers, with wheels
Wednesday 17 October 12
8. Imagine you never saw a car...
Would the following definitions help to explain it?
http://www.thefreedictionary.com/car
1. It’s an automobile
A phone that automatically takes a call..
2. It’s a vehicle, such as a streetcar
3. It’s a boxlike enclosure for passengers, with wheels
Wednesday 17 October 12
9. Imagine you never saw a car...
Would the following definitions help to explain it?
http://www.thefreedictionary.com/car
1. It’s an automobile
A phone that automatically takes a call..
2. It’s a vehicle, such as a streetcar
3. It’s a boxlike enclosure for passengers, with wheels
4. A chariot, carriage, or cart
Wednesday 17 October 12
10. Imagine you never saw a car...
Would the following definitions help to explain it?
http://www.thefreedictionary.com/car
1. It’s an automobile
A phone that automatically takes a call..
2. It’s a vehicle, such as a streetcar
3. It’s a boxlike enclosure for passengers, with wheels
4. A chariot, carriage, or cart
Wednesday 17 October 12
11. Imagine you never saw a car...
Would the following definitions help to explain it?
http://www.thefreedictionary.com/car
1. It’s an automobile
A phone that automatically takes a call..
2. It’s a vehicle, such as a streetcar
3. It’s a boxlike enclosure for passengers, with wheels
4. A chariot, carriage, or cart
A picture is worth a 1000 words
Wednesday 17 October 12
12. A definition...
Information Visualisation is the use of interactive
visual representations to amplify cognition [Card. et. al]
Wednesday 17 October 12
13. A definition...
Information Visualisation is the use of interactive
visual representations to amplify cognition [Card. et. al]
Find out what a data set is about
What are the stories behind the data?
Communicating data
Facilitate human interaction for exploration and understanding
Empower people to make informed decisions
Wednesday 17 October 12
15. Not new..
http://www.datavis.ca/milestones/
Wednesday 17 October 12
16. Not new..
http://www.datavis.ca/milestones/
Wednesday 17 October 12
17. Publication Networks in conferences
Who are the most prolific author(s)? Who is co-authoring with who?
Wednesday 17 October 12
18. Publication Networks in conferences
Who are the most prolific author(s)? Who is co-authoring with who?
Wednesday 17 October 12
19. Publication Networks in conferences
Who are the most prolific author(s)? Who is co-authoring with who?
Wednesday 17 October 12
20. Publication Networks in conferences
Who are the most prolific author(s)? Who is co-authoring with who?
Wednesday 17 October 12
21. Student Activity Meter
How are my students working? When do they work?
Are there students in trouble? ...
Wednesday 17 October 12
22. Student Activity Meter
How are my students working? When do they work?
Are there students in trouble? ...
Wednesday 17 October 12
23. Student Activity Meter
How are my students working? When do they work?
Are there students in trouble? ...
Wednesday 17 October 12
24. Step up!
Make students aware about their activity in the course
Wednesday 17 October 12
25. MUSE - Visualizing the origins and connections of institutions
based on co-authorship of publications
Nagel, T., Duval, E.: Muse:Visualizing the origins and connections of institutions based
on co-authorship of publications. Science2.0 for TEL workshop at EC-TEL 2010, Barcelona, Spain
Wednesday 17 October 12
26. On the menu...
graph
Some design basics
visualization
How to design a visualisation (application)?
Wednesday 17 October 12
27. What has the bigger share?
‘Real Estate’ or ‘Bonds’ has the bigger share?
http://www.perceptualedge.com/
Wednesday 17 October 12
28. What has the bigger share?
‘Real Estate’ or ‘Bonds’ has the bigger share?
Size & angle are not preattentive
http://www.perceptualedge.com/
Wednesday 17 October 12
29. “Save the pies for dessert” S. Few
What has the bigger share?
‘Real Estate’ or ‘Bonds’ has the bigger share?
Size & angle are not preattentive
http://www.perceptualedge.com/
Wednesday 17 October 12
40. DON’T USE VISUALISATIONS TO MISLEAD...
BP - leak in gulf of mexico
http://flowingdata.com/category/statistics/mistaken-data/
Wednesday 17 October 12
41. DON’T USE VISUALISATIONS TO MISLEAD...
BP - leak in gulf of mexico
http://flowingdata.com/category/statistics/mistaken-data/
Wednesday 17 October 12
42. DON’T USE VISUALIZATIONS TO LIE... (1/2)
http://www.perceptualedge.com/
http://flowingdata.com/category/statistics/mistaken-data/
Wednesday 17 October 12
43. DON’T USE VISUALIZATIONS TO LIE... (1/2)
http://www.perceptualedge.com/
http://flowingdata.com/category/statistics/mistaken-data/
Wednesday 17 October 12
44. DON’T USE VISUALIZATIONS TO LIE... (1/2)
http://www.perceptualedge.com/
http://flowingdata.com/category/statistics/mistaken-data/
Wednesday 17 October 12
45. DON’T USE VISUALIZATIONS TO LIE... (1/2)
http://www.perceptualedge.com/
http://flowingdata.com/category/statistics/mistaken-data/
Wednesday 17 October 12
46. DON’T USE VISUALIZATIONS TO LIE... (2/2)
http://flowingdata.com/category/statistics/mistaken-data/
Wednesday 17 October 12
47. DON’T USE VISUALIZATIONS TO LIE... (2/2)
http://flowingdata.com/category/statistics/mistaken-data/
Wednesday 17 October 12
48. USE COMMON SENSE (1/3)
Which of these line graphs is easier to read?
http://www.perceptualedge.com/
Wednesday 17 October 12
49. USE COMMON SENSE (2/3)
Which of these two tables is easier to read?
http://www.perceptualedge.com/
Wednesday 17 October 12
50. USE COMMON SENSE (3/3)
Which labels are easier to read?
http://www.perceptualedge.com/
Wednesday 17 October 12
51. Choose graphs that best communicates your data or
answer your questions about your data
Which graph makes it easier to focus on the pattern of change through
time, instead of the individual values?
http://www.perceptualedge.com/
Wednesday 17 October 12
52. THINK ABOUT WHAT YOU DO
Seems ok?
http://www.perceptualedge.com/
Wednesday 17 October 12
53. THINK ABOUT WHAT YOU DO
Seems ok?
http://www.perceptualedge.com/
Wednesday 17 October 12
54. THINK ABOUT WHAT YOU DO
Seems ok?
Equal interval scale
http://www.perceptualedge.com/
Wednesday 17 October 12
55. Which graph makes it easier to determine
R&Ds travel expense?
http://www.perceptualedge.com/
Wednesday 17 October 12
56. Which graph makes it easier to determine
R&Ds travel expense?
BE CAREFUL WITH 3D (DON’T USE IT)
http://www.perceptualedge.com/
Wednesday 17 October 12
57. On the menu...
Some graph design basics
visualization
How to design a visualisation (application)?
Wednesday 17 October 12
58. 2 Facts to keep in mind
Wednesday 17 October 12
59. 2 Facts to keep in mind
Humans have advanced perceptual abilities
Wednesday 17 October 12
60. 2 Facts to keep in mind
Humans have advanced perceptual abilities
Our brains makes us extremely good at recognizing visual patterns
Wednesday 17 October 12
61. 2 Facts to keep in mind
Humans have advanced perceptual abilities
Our brains makes us extremely good at recognizing visual patterns
Humans have little short term memory
Wednesday 17 October 12
62. 2 Facts to keep in mind
Humans have advanced perceptual abilities
Our brains makes us extremely good at recognizing visual patterns
Humans have little short term memory
Our brains remember relatively little of what we perceive
Wednesday 17 October 12
63. 2 Facts to keep in mind
Humans have advanced perceptual abilities
Our brains makes us extremely good at recognizing visual patterns
Make Use of Gestalt principles
Humans have little short term memory
Our brains remember relatively little of what we perceive
Wednesday 17 October 12
64. 2 Facts to keep in mind
Humans have advanced perceptual abilities
Our brains makes us extremely good at recognizing visual patterns
Make Use of Gestalt principles
Make it interactive, provide visual help
Humans have little short term memory
Our brains remember relatively little of what we perceive
Wednesday 17 October 12
66. Step 1: Think of a dataset,
Formulate the questions
Wednesday 17 October 12
67. Step 1: Think of a dataset,
Formulate the questions
“where” “when’’ “how much” “how often” (“why”)
Wednesday 17 October 12
68. Step 1: Think of a dataset,
Formulate the questions
“where” “when’’ “how much” “how often” (“why”)
Who are your intended users?
Wednesday 17 October 12
69. Example data-set :
Facebook privacy statement
Offer precise controls for sharing on the Internet...
Wednesday 17 October 12
70. Example data-set :
Facebook privacy statement
Offer precise controls for sharing on the Internet...
Users should navigate through 50 settings with more than 170 options
Wednesday 17 October 12
71. Example data-set :
Facebook privacy statement
Offer precise controls for sharing on the Internet...
Users should navigate through 50 settings with more than 170 options
Questions?
Wednesday 17 October 12
72. Example data-set :
Facebook privacy statement
Offer precise controls for sharing on the Internet...
Users should navigate through 50 settings with more than 170 options
Questions?
How did it change over time?
Wednesday 17 October 12
73. Example data-set :
Facebook privacy statement
Offer precise controls for sharing on the Internet...
Users should navigate through 50 settings with more than 170 options
Questions?
How did it change over time?
How does it compare to privacy statements of other tools?
Wednesday 17 October 12
74. Example data-set :
Facebook privacy statement
Offer precise controls for sharing on the Internet...
Users should navigate through 50 settings with more than 170 options
Questions?
How did it change over time?
How does it compare to privacy statements of other tools?
What are the options?
Wednesday 17 October 12
77. Step 2: Gather the dataset
eg. open data, census.gov, NY Times API, etc
Wednesday 17 October 12
78. Step 2: Gather the dataset
eg. open data, census.gov, NY Times API, etc
Define the characteristics of the data
Wednesday 17 October 12
79. Step 2: Gather the dataset
eg. open data, census.gov, NY Times API, etc
Define the characteristics of the data
Time? hierarchical? 1D? 2D? nD? network data?
Wednesday 17 October 12
80. Step 2: Gather the dataset
eg. open data, census.gov, NY Times API, etc
Define the characteristics of the data
Time? hierarchical? 1D? 2D? nD? network data?
scales?
Wednesday 17 October 12
81. Step 2: Gather the dataset
eg. open data, census.gov, NY Times API, etc
Define the characteristics of the data
Time? hierarchical? 1D? 2D? nD? network data?
scales?
https://www.facebook.com/about/privacy
Wednesday 17 October 12
82. Step 3: Apply a visual mapping
Wednesday 17 October 12
83. Step 3: Apply a visual mapping
Encode data characteristics into visual form
Wednesday 17 October 12
84. Step 3: Apply a visual mapping
Encode data characteristics into visual form
Simplicity is the ultimate sophistication.
Leonardo da Vinci
Wednesday 17 October 12
85. Size
most commonly used (?)
Wednesday 17 October 12
86. Colors
used for identifying patterns & anomalies in big datasets
Color Principles - Hue, Saturation, and Value
Wednesday 17 October 12
87. Gestalt Principles
¡ Law
of
Proximity
The closer objects are to each other,
the more likely they are to be
perceived as a group (Ehrenstein,
2004)
http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentation
Wednesday 17 October 12
88. Gestalt Principles
¡ Law
of
Proximity
The closer objects are to each other,
the more likely they are to be
perceived as a group (Ehrenstein,
2004)
http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentation
Wednesday 17 October 12
89. Gestalt Principles
¡ Law
of
Proximity
The closer objects are to each other,
the more likely they are to be
perceived as a group (Ehrenstein,
2004)
¡ Law
of
Symmetry
Objects must be balanced or symmetrical
to be seen as complete or whole (Chang,
2002).
http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentation
Wednesday 17 October 12
90. Gestalt Principles
¡ Law
of
Proximity
The closer objects are to each other,
the more likely they are to be
perceived as a group (Ehrenstein,
2004)
¡ Law
of
Symmetry
Objects must be balanced or symmetrical
to be seen as complete or whole (Chang,
2002).
http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentation
Wednesday 17 October 12
91. Gestalt Principles
¡ Law
of
Similarity
Objects that are similar, with like
components or attributes are more
likely to be organised together
(Schamber, 1986).
Objects are viewed in vertical rows because
of their similar attributes.
http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentation
Wednesday 17 October 12
92. Gestalt Principles
¡ Law
of
Similarity
Objects that are similar, with like
components or attributes are more
likely to be organised together
(Schamber, 1986).
Objects are viewed in vertical rows because
of their similar attributes.
¡ Law
of
Common
Fate
Objects with a common movement, that move
in the same direction, at the same pace , at the
same time are organised as a group
(Ehrenstein, 2004).
http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentation
Wednesday 17 October 12
93. Gestalt Principles
¡ Law
of
Similarity
Objects that are similar, with like
components or attributes are more
likely to be organised together
(Schamber, 1986).
Objects are viewed in vertical rows because
of their similar attributes.
¡ Law
of
Common
Fate
Objects with a common movement, that move
in the same direction, at the same pace , at the
same time are organised as a group
(Ehrenstein, 2004).
http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentation
Wednesday 17 October 12
94. Gestalt Principles
¡ Law
of
Similarity
Objects that are similar, with like
components or attributes are more
likely to be organised together
(Schamber, 1986).
Objects are viewed in vertical rows because
of their similar attributes.
¡ Law
of
Common
Fate
Objects with a common movement, that move
in the same direction, at the same pace , at the
same time are organised as a group
(Ehrenstein, 2004).
http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentation
Wednesday 17 October 12
95. Gestalt Principles
¡ Law
of
Continuation
Objects will be grouped as a whole if
they are co-linear, or follow a direction
(Chang, 2002; Lyons, 2001).
http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentation
Wednesday 17 October 12
96. Gestalt Principles
¡ Law
of
Continuation
Objects will be grouped as a whole if
they are co-linear, or follow a direction
(Chang, 2002; Lyons, 2001).
¡ Law
of
Isomorphism
Is similarity that can be behavioural or
perceptual, and can be a response based
on the viewers previous experiences
(Luchins & Luchins, 1999; Chang, 2002).
This law is the basis for symbolism
(Schamber, 1986).
http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentation
Wednesday 17 October 12
97. Gestalt Principles
¡ Law
of
Continuation
Objects will be grouped as a whole if
they are co-linear, or follow a direction
(Chang, 2002; Lyons, 2001).
¡ Law
of
Isomorphism
Is similarity that can be behavioural or
perceptual, and can be a response based
on the viewers previous experiences
(Luchins & Luchins, 1999; Chang, 2002).
This law is the basis for symbolism
(Schamber, 1986).
http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentation There are more!
Wednesday 17 October 12
98. Step 3: Apply a visual mapping
Shape - circles, rectangles, stars, icons,..
Location - maps
Network -node-link graphs
Time - animations
...
Wednesday 17 October 12
99. HOW DID IT CHANGE OVER TIME?
http://www.nytimes.com/interactive/2010/05/12/business/facebook-privacy.html
Wednesday 17 October 12
100. HOW DID IT CHANGE OVER TIME?
http://www.nytimes.com/interactive/2010/05/12/business/facebook-privacy.html
Wednesday 17 October 12
101. HOW DID IT CHANGE OVER TIME?
http://www.nytimes.com/interactive/2010/05/12/business/facebook-privacy.html
Wednesday 17 October 12
102. HOW DOES FB COMPARE
TO STATEMENTS OF OTHER TOOLS?
http://www.nytimes.com/interactive/2010/05/12/business/facebook-privacy.html
Wednesday 17 October 12
103. HOW DOES FB COMPARE
TO STATEMENTS OF OTHER TOOLS?
http://www.nytimes.com/interactive/2010/05/12/business/facebook-privacy.html
Wednesday 17 October 12
107. e.g. sketch on paper
e.g. what kind of filtering mechanisms?
Wednesday 17 October 12
108. Step 3: Apply a visual mapping to your dataset
e.g. sketch on paper
e.g. what kind of filtering mechanisms?
Wednesday 17 October 12
109. Step 3: Apply a visual mapping to your dataset
e.g. sketch on paper
Step 4: Think about interaction of visualisation app
e.g. what kind of filtering mechanisms?
Wednesday 17 October 12
110. Step 5: How to evaluate visualisations?
Build Usable & Useful Visualisations
Wednesday 17 October 12
111. Step 5: How to evaluate visualisations?
Typical HCI metrics don’t always work that well
•time required to learn the system
•time required to achieve a goal
•error rates
•retention of the use of the interface over time
Wednesday 17 October 12
112. Step 5: How to evaluate visualisations?
Not so easy: how to measure improved insights?
Typical HCI metrics don’t always work that well
•time required to learn the system
•time required to achieve a goal
•error rates
•retention of the use of the interface over time
Wednesday 17 October 12
113. Step 5: How to evaluate visualisations?
Not so easy: how to measure improved insights?
Typical HCI metrics don’t always work that well
•time required to learn the system
•time required to achieve a goal
•error rates
•retention of the use of the interface over time
Wednesday 17 October 12
115. Some metrics that can be used
• Effectiveness - does the visualization answer your questions? does it
provide value? Do they provide new insight? How? Why?
• Efficiency - to what extend may the visualization communicate your data
to the users efficiently? Do they get quicker answers to their questions?
• Usability - how easily the users interact with the system? Are the
information provided in clear and understandable format? Eg. Do the
layouts of elements make sense?
• Usefulness - are the visualizations useful? How may the users benefit from
it?
• Functionality - to what extend does the application provides the
functionalities required by the users?
Wednesday 17 October 12
116. Rapid Prototyping Time
Iteration 1 Iteration 2 Iteration 3 Iteration N
...
• Design focus on usefulness & usability
• target personas & scenarios
• Evaluate ideas in short iteration cycles
• e.g draw boundary box vs. contour of object of interest
• Evaluate in real-life settings
• with real users
44
Wednesday 17 October 12
117. Think aloud Usability lab Eye-tracking
questionnaires (SUS, TAM, ...)
Wednesday 17 October 12
118. Go outside your research lab
Evaluate in real-life settings
46
Wednesday 17 October 12
119. Go outside your research lab
Evaluate in real-life settings
Ec-tel 2010
Figure 4: Setting of the evaluation.
Hypertext 2011
Overview first, search & filter, Start with what you know,
details on demand then grow
46
Wednesday 17 October 12
122. To conclude..
Lets try to bust 2 myths in this course...
Wednesday 17 October 12
123. To conclude..
Lets try to bust 2 myths in this course...
Visualisations are just cool graphics
Wednesday 17 October 12
124. To conclude..
Lets try to bust 2 myths in this course...
Visualisations are just cool graphics
Graphics part of bigger picture of what stories to communicate & how
Wednesday 17 October 12
125. To conclude..
Lets try to bust 2 myths in this course...
Visualisations are just cool graphics
Graphics part of bigger picture of what stories to communicate & how
Only experts can create good visualizations
Wednesday 17 October 12
126. To conclude..
Lets try to bust 2 myths in this course...
Visualisations are just cool graphics
Graphics part of bigger picture of what stories to communicate & how
Only experts can create good visualizations
Maybe faster, but there are simple techniques anyone can apply
Wednesday 17 October 12
127. POINTERS
• http://wearecolorblind.com/articles/quick-tips/
• http://infosthetics.com
• http://www.visualcomplexity.com/vc/
• http://bestario.org/research/remap
• ... (a lot more online! )
Wednesday 17 October 12
129. FURTHER READINGS
• “Readings in Information Visualization: Using Vision to Think”,
Card, S et al
• “Now i see”, “Show Me the Numbers”, Few, S.
• “Beautiful Evidence”, Tufte, E.
• “Information Visualization. Perception for design”, Ware, C.
• Beautiful Visualization: Looking at Data through the Eyes of
Experts (Theory in Practice): Julie Steele, Noah Iliinsky
Wednesday 17 October 12
130. THANK YOU FOR YOUR
ATTENTION!
joris.klerkx@cs.kuleuven.be
@jkofmsk
52
Wednesday 17 October 12