Understanding learning in order to implement

efficient visualisation methods

Andy Kirkwww.visualisingdata.com

@visualisingdata

The stuff you need to learn to do data visualisation well

Andy Kirkwww.visualisingdata.com

@visualisingdata

Hebden Bridge

Data Visualisation Blogger

Data Visualisation Design Consultant

Data Visualisation Trainer

Data Visualisation Speaker

Data Visualisation Speaker

Data Visualisation Author

What you need to learnWhy you need to learn it

How to learn it

What are we covering?

http://image.yaymicro.com/rz_1210x1210/0/5d9/pile-of-bricks-5d9ac1.jpg

http://yourcolorcoach.files.wordpress.com/2010/11/img_7704.jpg

First, some eye candy

http://oecdbetterlifeindex.org/countries/united-kingdom/

OECD Better Life Index | Moritz Stefaner

http://derekwatkins.wordpress.com/2011/08/06/posted/

The Expansion of Post Offices Across the US | Derek Watkins

http://www.chrisjordan.com/gallery/rtn2/#gyre2

Running the Numbers II: Portraits of global mass culture | Chris Jordan

http://www.flickr.com/photos/visualizeyahoo/sets/72157629000570607/

Yahoo! C.O.R.E Data Visualization | Periscopic

http://hint.fm/wind/

Wind Map | Fernanda Viegas and Martin Wattenberg

The popular emergence of

data visualisation

http://www.google.com/insights/search/#q=%22Big%20Data%22%2CInfographics&date=6%2F2007%2058m&cmpt=q

PopularityGoogle Insights: Keyword Infographic

#1: DataPeriscopic: Yahoo! Mail Data Visualization

http://www.flickr.com/photos/visualizeyahoo/sets/72157627722660160/with/6235510547/

We are overwhelmed by data, not because there is too

much, but because we don't know

how to tame it.

[Paraphrasing] Stephen Few, perceptualedge.com

What’s missing?

http://eyeofestival.com/

#2: TechnologyThe ‘eyeo’ Festival (2011-2012)

Doing data visualisation well is

less a technology problem, more a people problem.Paraphrasing Aron Pilhofer, New York Times

What’s missing?

#3: ExposureHans Rosling: TEDTalks “Myths about the developing world“

(2006)

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

The skills required for most effectively displaying

information are not intuitive and rely largely on

principles that must be learned.

Stephen Few, ‘Show Me the Numbers’

What’s missing?

Heuristics vs. Principles

Should/could vs. Must

What’s missing?

The representation and presentation of data that

exploitsour visual perception

abilities in order to amplify cognition

What is data visualisation?

http://upload.wikimedia.org/wikipedia/commons/thumb/0/04/Human_Brain_sketch_with_eyes_and_cerebrellum.svg/1000px-Human_Brain_sketch_with_eyes_and_cerebrellum.svg.png

Visual Cortex

Seeing

Cerebral Cortex

Thinking

Messenger Encode DecodeMessage Receiver

Inspiration Insight

Understanding

Persuasion

Ideas DiscoveriesComplexiti

esResults

Skills and Knowledge

Multi-disciplinary: Art & Science

Images from http://psychology.about.com/od/sensationandperception/ss/gestaltlaws.htm

Cognitive Science: Gestalt Laws

Cognitive Science: Gestalt Laws

http://www.mirror.co.uk/sport/football/euro-2012-where-italy-will-place-their-penalties-907506

http://en.wikipedia.org/wiki/Ebbinghaus_illusion

Cognitive Science: Illusions

http://www.leancrew.com/all-this/2011/11/i-hate-stacked-area-charts/

Cognitive Science: Illusions

Visible pixels on left graph: blue = 82% pink =18%

Visible pixels on right graph: blue = 91% pink = 9%

Office for National Statistics: Presentation by Alan Smith, “The Curious Incident of Kevins in Zurich…and other stories”

Cognitive Science: Deceptions

$0.8M out of $7.5M = 10.7%Length of presented bar progress = 24.6%

Cognitive Science: Deceptions

https://donate.wikimedia.org.uk/

http://www.visualisingdata.com/index.php/2011/09/distorted-and-misleading-graphics-on-sky-sports/

Cognitive Science: Deceptions

http://driven-by-data.net/about/chromajs/#/0 | http://colorbrewer2.org/ | http://www.amazon.co.uk/Visual-Thinking-Kaufmann-Interactive-Technologies/dp/0123708966

Cognitive Science: Colour theory

Cognitive Science: Visual Variables

Colour

Length

Blur/Focus

Radius/DiameterSlope

Luminance

Height

Orientation

Area

Angle

Curvature/Arc

Volume

Motion

Texture

Transparency

Shape

Glyph

Position

Label

Saturation

Size

Speed

Direction

Flow

Original – J. D. MacKinlay, ‘Automating the design of graphical presentations of relational information’, 1986 | Redesign - Joe Parry

Cognitive Science: Visual Variables

Analytical/Pragmatic

Abstract/Emotive

Exp

lan

ato

ryExp

lora

tory

Design: Visualisation Context

Design: Chart Types

Design: Typography

http://www.visualisingdata.com/index.php/2012/07/improving-my-knowledge-on-typography-in-data-visualisation/

Design: Instinct

http://graphics-info.blogspot.hk/2012/09/malofiej-20-look-at-our-participation.html

http://moritz.stefaner.eu/projects/musli-ingredient-network/

Chose the chord diagram over the possibly more revealing matrix design because the matrix doesn't look “tasty” and “muesli

shouldn't look like fungi”

Design: Instinct

http://collider.com/wp-content/uploads/WarGames-Sheedy-and-Broderick-on-computer.jpg

Computers: Software/Programming

Computers: HCI/UX

http://max-planck-research-networks.net/

Computers: Digital Cartography

http://www.nasa.gov/topics/earth/features/perpetual-ocean.html

Data: Databases, Wrangling

http://datamarket.com/ | http://www.flickr.com/photos/visualizeyahoo/sets/72157627722660160/with/6235510547/ | http://code.google.com/p/google-refine/

Data: Maths & Statistical Analysis

http://www.getstats.org.uk/ | http://kartograph.org/

How to learn and where from?

Practice, practice, practice – experience is the key

Seek potential projects – paid, curiosity, contests

Learn about yourself – take notes, self critique

Technical skills – push yourself out of comfort zone

Evaluate others – silently or provide reviews

Publish yourself – encourage and digest peer critique

Craft

Online content – immerse yourself in the community

Books – so many invaluable references and inspirations

Academia – papers, journals

Conferences – within the field and around it

Training/education – look for good training provider…

Theory

The 8 Hats of Data Visualisation

Initiator Journalist CommunicatorProject

Manager

DesignCognitive Science

ComputerScience

DataScience

Initiator = Legs

Designer = Eye

Data Scientist = Back

Journalist = Nose

Cognitive Scientist = Mind

Project Manager = Torso

Communicator = Mouth & Ears

Computer Scientist = Hands

www.visualisingdata.comandy@visualisingdata.co

m@visualisingdata