Visualisation - introduction, guidelines, principles and design

Post-‐academic course Big Data

Joris KlerkxResearch Expert, PhD.joris.klerkx@cs.kuleuven.be@jkofmsk

Erik DuvalProfessorerik.duval@cs.kuleuven.be@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).

Augment group - HCI research lab Dept. ComputerwetenschappenKU Leuvenhttps://augmenthuman.wordpress.com

Technology Enhanced Learning

e-health

Research 2.0

HealthMedia

(Consumption)

Technology Enhanced Learning

Science 2.0

http://eng.kuleuven.be/datavislab/3

Before break: - Examples- General guidelines while using visualisation techniques

After Break:- Perception, Design & Design aesthetics

http://www.informationisbeautiful.net/visualizations/how-many-gigatons-of-co2/

http://www.hearts.com/ecolife/cut-paper-consumption-protect-forests/

Slides will be posted to Slideshare & Zephyr6

DATA ABUNDANCE - BIG DATA

+/- 40% of world population8

How to create value from of such data?

How to generate insights from this data?

How to facilitate human interaction for exploration with and understanding of data?

12Source: Andrew Vande Moere

Why visualisation ?

algorithm<>

data mining<>

visual analytics

number crunching

<>human

perception

self driving car<>

gps + dashboard

Why visualisation ?

Anscombe`s quartethttp://en.wikipedia.org/wiki/Anscombe's_quartet

Enables discovery of visual patterns in data setsGraphics reveal data (Tufte, 2001)

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?

Communicates data easily

http://terror.periscopic.com

Shows patterns & triggers questions

http://blog.stephenwolfram.com/2012/03/the-personal-analytics-of-my-life/

Shows trends & anomalies in the data, therefore triggers questions

Helps to find stories, see trends

BelgiumBrazil

Sentiment analysis in enterprise social network (slack)

Triggers questions & creates awareness

Should we trust SOTA NLP-algorithms?

Empowers users to make informed decisions

Positive Badges

Negative Badges

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

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

http://infosthetics.com/

http://visualizing.orghttp://www.visualcomplexity.com/vc/

http://visual.ly/

http://flowingdata.comhttp://www.infovis-wiki.net

Defining visualisation

Information Visualisation is the use of interactive visual representations to amplify cognition [Card. et. al]

Definition

Visualization

Slide source: John Stasko

Scientific visualization

Information visualization

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

http://www.visual-analytics.eu/faq

Also: Visual Analytics

Guidelines & Facts

How many circles?

Humans have advanced perceptual abilitiesOur brains makes us extremely good at recognizing visual patterns

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

http://infovis-lvm.github.io

Overview first, zoom & filter, details-on-demand

Real data is ugly and needs to be cleaned

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

http://nieuws.vtm.be/verkiezingen/gemeente?province=P1&city=G73

Always check & pre-process your data

Verkiezingen 14/10/12

Forget about 3D graphs (on a 2D screen..)

Occlusion Complex to interact with Doesn’t add anything to the data

Source: Stephen Few

What if we need to add a 3rd variable?

Use small coordinated graphs to add variables

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

Source: Stephen Few

Save the pies for dessert (S. Few)

Try using either of the pies to put the slices in order by size

deredactie.be

demorgen.be

vtm.be

Obviously there are exceptions to the rule

55http://themetapicture.com/the-sunny-side-of-the-pyramid/

blogposts" tweets" comments"on"blogs"

reports"submi6ed"

Student'1'

Student"1"

0" 5" 10" 15" 20" 25" 30"

blogposts"

comments"on"blogs"

tweets"

reports"submi6ed"

Student'1'

Student"1"

Use Common Sense

blogposts" comments"on"blogs"

tweets" reports"submi6ed"

Student'1'

Student"1"

0" 10" 20" 30" 40" 50" 60"

Student"1"

Student"2"

Student"3"

Student"4"

blogposts"

tweets"

comments"on"blogs"

reports"submi:ed"

0%# 20%# 40%# 60%# 80%# 100%#

Student#1#

Student#2#

Student#3#

Student#4#

blogposts#

tweets#

comments#on#blogs#

reports#submi;ed#

Use Common Sense

What are you comparing?What story do you get from it?

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

Communicate the correct story

Don’t use visualisations to mislead

Source: Stephen Few 62

Source: Stephen Few 63

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

http://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen.html69

Human Perception

Our brains makes us extremely good at recognizing visual patterns

Source: Katrien Verbert 71

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.

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

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

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

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

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).

http://www.slideshare.net/chelsc/gestalt-laws-and-design-presentation

London Tube Map

Which Gestalt laws do you see?

Design

How to create your visualization? a workflow

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

- structuretime, hierarchy, network, 1D, 2D, nD, …

- questions where, when, how often, …

- audience domain & visualisation expertise, …

S. Stevens. On the theory of scales of measurement. Science, 103(2684), 1946.

StructureTime? hierarchical? 1D? 2D? nD? network? …

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?)

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

How much bigger is the lower bar?

Slide adapted from Michael Porath & Katrien Verbert

Length

How much bigger is the right circle?

Slide adapted from Michael Porath & Katrien Verbert

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 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

How to choose colors

http://colorbrewer2.org

Position

Position & color

http://time.com/12933/what-you-think-you-know-about-the-web-is-wrong/

J. Mackinlay. Automating the design of graphical presentations of relational information. ACM Transactions On Graphics, 5(2):110–141, 1986.

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?

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 }

http://blog.visual.ly/45-ways-to-communicate-two-quantities/108

Design aesthetics

Data ink design principles

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)

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)

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

What is the data-ink ratio?

< 0.05

What is the data-ink ratio of this graphic?

< 0.001

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

5. Revise and edit.

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.

5. Revise and edit.

119 source: Joey Cherdarchuk

“Perfection is achieved not when there is nothing more to add, but when there is nothing left to take away”

– Antoine de Saint-Exupery

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

?Joris KlerkxResearch Expert, PhD.joris.klerkx@cs.kuleuven.be@jkofmsk

https://augmenthuman.wordpress.com

Visualisation - introduction, guidelines, principles and design

Education

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