visualisation - introduction, guidelines, principles and design
TRANSCRIPT
Post-‐academic course Big Data
Post-‐academic course Big Data
Joris KlerkxResearch Expert, [email protected]@jkofmsk
Erik [email protected]@erikduval
VisualisatieBig Data - module 3IVPV - Instituut voor Permanente Vorming28-05-2015
To research, design, create and evaluate useful tools that augment the human intellect
By ‘augmen+ng human intellect’ we mean increasing the capability of a man to approach a complex problem situa+on, to gain comprehension to suit his particular needs, and to derive solu+ons to problems (Douglas Engelbart, 1962).
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Augment group - HCI research lab Dept. ComputerwetenschappenKU Leuvenhttps://augmenthuman.wordpress.com
Music
Technology Enhanced Learning
e-health
Research 2.0
HealthMedia
(Consumption)
Technology Enhanced Learning
Science 2.0
http://eng.kuleuven.be/datavislab/3
Today
Before break: - Examples- General guidelines while using visualisation techniques
After Break:- Perception, Design & Design aesthetics
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http://www.informationisbeautiful.net/visualizations/how-many-gigatons-of-co2/
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http://www.hearts.com/ecolife/cut-paper-consumption-protect-forests/
Slides will be posted to Slideshare & Zephyr6
DATA ABUNDANCE - BIG DATA
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+/- 40% of world population8
How to create value from of such data?
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How to generate insights from this data?
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How to facilitate human interaction for exploration with and understanding of data?
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12Source: Andrew Vande Moere
Why visualisation ?
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algorithm<>
human
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data mining<>
visual analytics
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number crunching
<>human
perception
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self driving car<>
gps + dashboard
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Why visualisation ?
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Anscombe`s quartethttp://en.wikipedia.org/wiki/Anscombe's_quartet
Enables discovery of visual patterns in data setsGraphics reveal data (Tufte, 2001)
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World Population GrowthA tremendous change occurred with the industrial revolution: whereas it had taken all of human history until around 1800 for world population to reach one billion, the second billion was achieved in only 130 years (1930), the third billion in less than 30 years (1959), the fourth billion in 15 years (1974), and the fifth billion in only 13 years (1987). During the 20th century alone, the population in the world has grown from 1.65 billion to 6 billion.
Seeing is understanding21
Facilitates understanding22
http://www.bbc.co.uk/news/world-15391515
Facilitates human interaction for exploration and understanding23
http://www.bbc.co.uk/news/world-15391515
Will there be enough food?
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Communicates data easily
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http://terror.periscopic.com
Shows patterns & triggers questions
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http://blog.stephenwolfram.com/2012/03/the-personal-analytics-of-my-life/
Shows trends & anomalies in the data, therefore triggers questions
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Helps to find stories, see trends
BelgiumBrazil
USA27
India
Sentiment analysis in enterprise social network (slack)
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Sentiment analysis in enterprise social network (slack)
Triggers questions & creates awareness
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Should we trust SOTA NLP-algorithms?
Empowers users to make informed decisions
Positive Badges
Negative Badges
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Khaled Bachour, Frederic Kaplan, Pierre Dillenbourg, "An Interactive Table for Supporting Participation Balance in Face-to-Face Collaborative Learning," IEEE Transactions on Learning Technologies, vol. 3, no. 3, pp. 203-213, July-September, 2010
Creates awareness
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T. Nagel, M. Maitan, E. Duval, A. Vande Moere, J. Klerkx, K. Kloeckl, and C. Ratti. Touching transport - a case study on visualizing metropolitan public transit on interactive tabletops. In AVI2014: 12th ACM International Working Conference on Advanced Visual Interfaces, pages 281–288, 2014.
http://www.youtube.com/watch?v=wQpTM7ASc-w
Facilitates human interaction for exploration and understanding
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http://infosthetics.com/
http://visualizing.orghttp://www.visualcomplexity.com/vc/
http://visual.ly/
http://flowingdata.comhttp://www.infovis-wiki.net
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Defining visualisation
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Information Visualisation is the use of interactive visual representations to amplify cognition [Card. et. al]
Definition
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Visualization
Slide source: John Stasko
Scientific visualization
Information visualization
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Information Visualisation
Concerned with data that does not have a well-defined representation in 2D or 3D space (i.e., “abstract data”)
Slide source: Robert Putman 37
Scientific visualisation
Specifically concerned with data that has a well-defined representation in 2D or 3D space (e.g., from simulation mesh or scanner).
Slide source: Robert Putman 38
Guidelines & Facts
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How many circles?
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Humans have advanced perceptual abilitiesOur brains makes us extremely good at recognizing visual patterns
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Humans have little short term memoryOur brain remembers relatively little of what we perceive.Most of us can only hold three to seven chunks of data at the same time.
Visual Information Seeking Mantra
https://www.youtube.com/watch?v=og7bzN0DhpI (9:51 - 11:22 )44
http://www.bbc.com/future/bespoke/20140724-flight-risk/
Overview first, zoom & filter, details-on-demand
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http://infovis-lvm.github.io
Overview first, zoom & filter, details-on-demand
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Real data is ugly and needs to be cleaned
http
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http://www.netmagazine.com/features/seven-dirty-secrets-data-visualisationhttps://code.google.com/p/google-refine/
http://vis.stanford.edu/wrangler/Pre-process your data
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http://nieuws.vtm.be/verkiezingen/gemeente?province=P1&city=G73
Always check & pre-process your data
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Verkiezingen 14/10/12
Forget about 3D graphs (on a 2D screen..)
Occlusion Complex to interact with Doesn’t add anything to the data
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Source: Stephen Few
What if we need to add a 3rd variable?
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Use small coordinated graphs to add variables
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Forget about 3D graphs
Source: Stephen Few
Which student has more blogposts?
• Size & angle are difficult to compare• Without labels & legends, impossible to show exact quantitative
differences• Limited Short term (visual) memory
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Source: Stephen Few
Save the pies for dessert (S. Few)
Try using either of the pies to put the slices in order by size
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deredactie.be
demorgen.be
vtm.be
Verkiezingen 14/10/12
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Obviously there are exceptions to the rule
55http://themetapicture.com/the-sunny-side-of-the-pyramid/
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blogposts" tweets" comments"on"blogs"
reports"submi6ed"
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Use Common Sense
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Use Common Sense
What are you comparing?What story do you get from it?
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Which graph makes it easier to focus on the pattern of change through time, instead of the individual values?
Choose graph that answers your questions about your data58Source: Stephen Few
vtm.be
deredactie.be
nieuwsblad.be
Verkiezingen 14/10/12
Communicate the correct story
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Don’t use visualisations to mislead
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Don’t use visualisations to mislead
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Source: Stephen Few 62
Source: Stephen Few 63
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http://fellinlovewithdata.com/research/deceptive-visualizations 65
http://fellinlovewithdata.com/research/deceptive-visualizations 66
How much better are the drinking water conditions in Willowtown as compared to Silvatown?
67http://fellinlovewithdata.com/research/deceptive-visualizations
Another example
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http://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen.html69
Human Perception
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Our brains makes us extremely good at recognizing visual patterns
Source: Katrien Verbert 71
Source: Katrien Verbert 72
A limited set of visual properties that are detected - very rapidly (< 200 to 250 ms), - accurately,- with little effort,- before focused attentionby the low-lever visual system on them.
Healey, C., & Enns, J. (2012). AFenGon and Visual Memory in VisualizaGon and Computer Graphics. IEEE Transac+ons on Visualiza+on and Computer Graphics , 18 (7), 1170-‐1188.
Pre-attentive characteristics
Note that eye movements take at least 200 ms to initiate.
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Pre-attentive characteristics
Find the red dot
<> Hue
Find the dot
<> shape
Find the red dot
conjunction not pre-attentive
http://www.csc.ncsu.edu/faculty/healey/PP/
helps to spot differences in multi-element display
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Pre-attentive characteristics
Line orientation Length, width Closure Size
Curvature Density, contrast Intersection 3D depth
Not all of them allow showing exact quantitative differencesHelps to spot differences in multi-element display
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http://www.csc.ncsu.edu/faculty/healey/PP/
http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentation
http://artspilesenglish.blogspot.be/2011/11/gestalt-theory-exercise-for-3rdlevel.html
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Gestalt Laws (“Pattern” laws)
Basic rules or design principles that describe perceptual phenomena.Explain the way users or humans see patterns in visualisations.
Figure & Ground
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Smallness
78Source: Katrien Verbert
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).
79http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentation
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).
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http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentation
London Tube Map
Which Gestalt laws do you see?
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Design
How to create your visualization? a workflow
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B. McDonnel and N. Elmqvist. Towards utilizing gpus in information visualization: A model and implementation of image-space operations. Visualization and Computer Graphics, IEEE Transactions on, 15(6):1105–1112, 2009.http://www.infovis-wiki.net/index.php/Visualization_Pipeline
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Data
- structuretime, hierarchy, network, 1D, 2D, nD, …
- questions where, when, how often, …
- audience domain & visualisation expertise, …
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S. Stevens. On the theory of scales of measurement. Science, 103(2684), 1946.
StructureTime? hierarchical? 1D? 2D? nD? network? …
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Questions (to get things going)
What is the average amount of students that bought the course book ?
What? When? How much? How often?
When did students start looking at the course material?
How much hours did Peter work on this assignment?
(Why did Peter have to redo his assignment?)
How often did Peter retake the course before he passed?
(why?)
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Visual mapping
Encode data characteristics into visual form
Each mark (point, line, area,…) represents a data element
Think about relationships between elements (position)
“Simplicity is the ultimate sophistication.”Leonardo da Vinci
Size
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X 4
How much bigger is the lower bar?
Slide adapted from Michael Porath & Katrien Verbert
Length
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X 5
How much bigger is the right circle?
Slide adapted from Michael Porath & Katrien Verbert
Area
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X 9
How much bigger is the right circle?
91Slide adapted from Michael Porath & Katrien Verbert
Apparent magnitude curves
http://makingmaps.net/2007/08/28/perceptual-‐scaling-‐of-‐map-‐symbols
Slide adapted from Michael Porath 92
Which one is more accurate?
Slide adapted from Michael Porath 93
Compensating magnitude to match perception
Color
Color Principles - Hue, Saturation, and Value
https://www.youtube.com/watch?v=l8_fZPHasdo94
Use maximum +/- 5 colors (for categories,.. ) (short term memory)
http://en.wikipedia.org/wiki/HSL_and_HSV
• hue: categorical
• saturation: ordinal and quantitative
• luminance: ordinal and quantitative
How to choose colors
source from: Katrien Verbert 95
http://gizmodo.com/why-a-white-cup-makes-your-coffee-taste-more-intense-1663691154
intensity, sweetness, aroma, bitterness, and quality
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How to choose colors
http://colorbrewer2.org
Position
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Position & color
http://time.com/12933/what-you-think-you-know-about-the-web-is-wrong/
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J. Mackinlay. Automating the design of graphical presentations of relational information. ACM Transactions On Graphics, 5(2):110–141, 1986.
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J. Mackinlay. Automating the design of graphical presentations of relational information. ACM Transactions On Graphics, 5(2):110–141, 1986.
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Offer precise controls for sharing on the Internet... Users should navigate through 50 settings with more than 170 options
Example Facebook privacy statement
Questions?
How did its complexity change over time? How does its length compare to privacy statementsof other tools?
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How did its complexity change over time?
http://www.nytimes.com/interactive/2010/05/12/business/facebook-privacy.html104
How does its length compare to privacy statementsof other tools?
http://www.nytimes.com/interactive/2010/05/12/business/facebook-privacy.html105
Example: Encoding weather forecast on a smartphone
106 http://partlycloudy-app.com
EXERCISE
Find all possible ways to visualize a small data set of two numbers { 75, 37 }
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http://blog.visual.ly/45-ways-to-communicate-two-quantities/108
Design aesthetics
Data ink design principles
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Five principles
1. Above all else show the data.
2. Maximize the data-ink ratio, within reason.
3. Erase non-data ink, within reason.
4. Erase redundant data-ink.
5. Revise and edit.
Source: Katrien Verbert
"The success of a visualization is based on deep knowledge and care about the substance, and the
quality, relevance and integrity of the content." (Tufte, 1983)
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Data-ink
“A large share of ink on a graphic should present data information, the ink changing as the data change. Data-ink is
the non-erasable core of a graphic...” (Tufte,1983)
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Data-ink ratio = data-ink
Total ink used to print graphic
= Proportion of a graphic’s ink devoted to the non-redundant display of data-information.
= 1.0 – proportion of graphic that can be erased without the loss of information
Data-ink ratio
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Data-ink ratio
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What is the data-ink ratio?
< 0.05
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What is the data-ink ratio of this graphic?
< 0.001
Source: Katrien Verbert 115
Five Principles1. Above all else show the data.
2. Maximize the data-ink ratio.
• Within reason
• Every bit of ink on a graphic requires a reason
3. Erase non-data ink, within reason.
4. Erase redundant data-ink.
5. Revise and edit.
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Maximize the data-ink ratio, within reason
“A pixel is a terrible thing to waste.”
(Shneiderman)
Slide source: Chris North, Virginia Tech 117
Five Principles
1. Above all else show the data.
2. Maximize the data-ink ratio, within reason.
3. Erase non-data ink, within reason.
4. Erase redundant data-ink.
5. Revise and edit.
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119 source: Joey Cherdarchuk
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“Perfection is achieved not when there is nothing more to add, but when there is nothing left to take away”
– Antoine de Saint-Exupery
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Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods. William S. Cleveland; Robert McGill (PDF)
7 foundational papers
The Structure of the Information Visualization Design Space. Stuart K. Card and Jock Mackinlay (PDF)
Visual Information Seeking: Tight Coupling of Dynamic Query Filters with Starfield Displays. Christopher Ahlberg and Ben Shneiderman (PDF)
High-Speed Visual Estimation Using Preattentive Processing. C. G. Healey, K. S. Booth and J. T. Enns (PDF)
Automating the Design of Graphical Presentations of Relational Information. Jock Mackinlay (PDF)
How NOT to Lie with Visualization. Bernice E. Rogowitz, Lloyd A. Treinish (PDF).
The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. Ben Shneiderman (PDF).
http://fellinlovewithdata.com/guides/7-classic-foundational-vis-papers
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