1. Understanding learning in
order to implement efficient
visualisation methods
Andy Kirk
www.visualisingdata.com
@visualisingdata

2. The stuff you need to learn to
do data visualisation well
Andy Kirk
www.visualisingdata.com
@visualisingdata

3. Hebden Bridge

4. Data Visualisation Blogger

5. Data Visualisation Design
Consultant

6. Data Visualisation Trainer
ata Visualisation
Training Courses Introduction to D
le
Current Public Schedu
Visual isation
The Growth of Data
middle of 2012:
public training course through to the
means for These are the scheduled
ded us with ubiquitous Arts, Copenhagen | £250
COP2
in technology have provi nts of data. Where once
data sh Academy of Fine
Exponential advances lising incredible amou Thu 8 Mar | Royal Dani Arts, Copenhagen | £250
COP1
ing, recording and mobi attitudes as consumers
have sh Academy of Fine
creat dance. Our Fri 9 Mar | Royal Dani on | £235 LON3
was scarce, now it is captured in abun for visual insight e, University of Lond
openness and yearn Thu 26 Apr | Senate
Hous
Y, New York City | £250
NYC1
nd transparency and ol of Journalism, CUN
also evolved: we dema Fri 11 May | Grad Scho n DC | £250 WDC1
to aid our understan
ding. n Center, Washingto
for the Mon 14 May | Foundatio £250 BAL1
widespread capabilities Wed 16 May go | £250 CHI1
s to fantastic tools and iques required Center Conference, Chica
Yet, whilst we have acces knowledge and techn Fri 15 Jun | University
analysis of data, the Toronto | £250 TOR1
storage, handling and Mon 18 Jun | Venue TBC, £235 BRS1
instinct
ach based on intuition, Fri 29 Jun Edinburgh | £235 EDI1
e world, a design appro n Hotel, University of
a cluttered, competitiv Fri 6 Jul | Salisury Gree AMS1
Amsterdam | £250
e data visualisati on comes in. Fri 13 Jul | Venue TBC,
overload. This is wher
al A 10% discount
comm unications that appe Training page on
and innovation, designing
unleashing creativity ter to attend an event.
brains process .com where you can also regis
and exploiting the way our eyes and www.visualisingdata
aimed at understanding
recent times
th in popularity over
lisation and its grow sizes and
The interest in data visua isations of all shapes,
e story. As a result, organ ister now to reserve a
Places are limited so reg
has been a remarkabl tial value.
ng up to the realisation of its poten
domain are now waki workshop.
place on your preferred training
nt
Training Course Conte Visit the www.visualisin
gdata.com, select the
rehensive, tion.
The objective of the
training is to provi de delegates with a comp
Training page and click on your preferred loca
excitement
events buzzing with
ition. You will leave the have acquired,
impact and amplify cogn ical capabilities you
knowledge and pract s and opportunities
about the foundation visualisation challenge
Further Information
on future data
inspiring you to take
include: environment
ed in the courses will Class size a supportive learning
The main topics cover size is 20 to facilitate
xt of data visualisation The maximum class
d and modern conte
Historical backgroun an visual system een all attendees.
of design and the hum group discussion betw
Foundation principles selection
design and
The essentials of chart
and resources Refreshments
tial visualisation tools held in city central locat
ions.
Exploration of the essen process ded. All events will be
n methodology and lunch will not be inclu
The visualisation desig on design
ing to visualisati
Applying critical think itioners
ice examples and pract Laptops
Showcase of best pract
case studies
Visualisation project
lisation challenges g the day’s activities.
re your own data visua across the group durin
Opportunities to explo have a some devices
Times
? end of the
Who Should Attend
time allocated at the
g from 9:00 and extra
registration commencin er discussions.
is interested in questions or hold furth
responsibility for, or session to pick up any
d for anyone who has data.
The courses are suite and communicating
s for visually exploring
best practice approache
.
Visualising Data Ltd
who
lex datasets, or somebody
st with large and comp t be an
You might be an analy gement report. You migh
the occasional mana Ltd, a UK based data
visualisation
just wants to enhance er of Visualising Data ber of this
from the crowd. You
might be a Andy Kirk is the found has been an active mem
looking to stand out ing skills. training service. He
to advertising and are ner without programm design consultancy and
design training or a desig r.
programmer with no g or the public secto
cine, the media, engineerin popular blog www.visua
lisingdata.com.
You might be in medi
we’ve all
Data is everywhere and
is no typical delegate. is most
The point is that there . Anyone and everyone
with it, so let’s do it right
got to do something d!
d to atten
welcome and encourage

7. Data Visualisation Speaker

8. Data Visualisation Speaker

9. Data Visualisation Author

10. What are we covering?
What you need to learn
Why you need to learn it
How to learn it

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

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

13. First, some eye candy

15. OECD Better Life Index | Moritz Stefaner
http://oecdbetterlifeindex.org/countries/united-kingdom/

16. The Expansion of Post Offices Across the US | Derek Watkins
http://derekwatkins.wordpress.com/2011/08/06/posted/

17. Running the Numbers II: Portraits of global mass culture | Chris Jordan
http://www.chrisjordan.com/gallery/rtn2/#gyre2

18. Yahoo! C.O.R.E Data Visualization | Periscopic
http://www.flickr.com/photos/visualizeyahoo/sets/72157629000570607/

19. Wind Map | Fernanda Viegas and Martin Wattenberg
http://hint.fm/wind/

20. The popular emergence of
data visualisation

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

22. #1: Data
Periscopic: Yahoo! Mail Data Visualization
http://www.flickr.com/photos/visualizeyahoo/sets/72157627722660160/with/6235510547/

23. What’s missing?
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

24. #2: Technology
The „eyeo‟ Festival (2011-2012)
http://eyeofestival.com/

25. What’s missing?
Doing data visualisation well is
less a technology problem,
more a people problem.
Paraphrasing Aron Pilhofer, New York Times

26. #3: Exposure
Hans Rosling: TEDTalks “Myths about the developing world“ (2006)
http://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen.html

27. What’s missing?
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‟

28. What’s missing?
Heuristics vs. Principles
Should/could vs. Must

31. What is data visualisation?
The representation and
presentation of data that exploits
our visual perception abilities in
order to amplify cognition

32. Cerebral Cortex Visual Cortex
Thinking Seeing
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

33. Ideas Inspiration
Discoveries Insight
Complexities Understanding
Results Persuasion
Messenger Encode Message Decode Receiver

34. Skills and Knowledge

35. Multi-disciplinary: Art & Science

36. Cognitive Science: Gestalt Laws
Images from http://psychology.about.com/od/sensationandperception/ss/gestaltlaws.htm

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

38. Cognitive Science: Illusions
http://en.wikipedia.org/wiki/Ebbinghaus_illusion

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

40. Cognitive Science: Deceptions
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”

41. Cognitive Science: Deceptions
$0.8M out of $7.5M = 10.7%
Length of presented bar progress = 24.6%
https://donate.wikimedia.org.uk/

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

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

44. Cognitive Science: Visual Variables
Length Volume
Size
Area Texture Colour Label
Direction
Saturation Position
Slope
Height Angle Radius/Diameter
Speed
Curvature/Arc Shape
Orientation
Transparency
Luminance Glyph
Flow Motion
Blur/Focus

45. Cognitive Science: Visual Variables
Original – J. D. MacKinlay, „Automating the design of graphical presentations of relational information‟, 1986 | Redesign - Joe Parry

46. Design: Visualisation Context
Explanatory Analytical/Pragmatic
Exploratory
Abstract/Emotive

47. Design: Chart Types

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

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

50. Design: Instinct
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”
http://moritz.stefaner.eu/projects/musli-ingredient-network/

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

52. Computers: HCI/UX
http://max-planck-research-networks.net/

53. Computers: Digital Cartography
http://www.nasa.gov/topics/earth/features/perpetual-ocean.html

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

55. Data: Maths & Statistical Analysis
http://www.getstats.org.uk/ | http://kartograph.org/

56. How to learn and where
from?

57. Craft
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

58. Theory
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…

59. The 8 Hats of Data Visualisation
Project
Initiator Journalist Communicator
Manager
Cognitive Design Computer Data
Science Science Science

60. Cognitive Scientist = Mind
Designer = Eye
Journalist = Nose
Communicator = Mouth & Ears
Computer Scientist = Hands
Data Scientist = Back
Project Manager = Torso
Initiator = Legs

61. www.visualisingdata.com
andy@visualisingdata.com
@visualisingdata