This document discusses information graphics and data visualization. It provides examples of different types of charts, graphs, maps, and diagrams that have been used throughout history to visualize data. These include charts created by William Playfair in the late 18th century, ISOTYPE diagrams developed by Otto and Marie Neurath, and Sankey diagrams. The document also discusses different tools that can be used to create visualizations, including standard programs like Excel, specialized tools like Tableau and Raw, or developing custom visualizations using programming languages. Finally, it provides some examples of visualizations and discusses reasons for visualizing data like efficient communication or enabling comparisons.

1. Dr Will Stahl-Timmins
The Farr Institute
16 May 2016
INFORMATION GRAPHICS
in context

2. information graphics
(dataviz)
(infographics)
(data journalism)

3. charts / graphs
7%
8%
10%
11%
29%
35%
0
25
50
75
100
2007 2008 2009 2010
0
25
50
75
100
2007 2008 2009 2010

4. charts / graphs

5. charts / graphs
William Playfair
(1759–1823)

6. charts / graphs

7. maps / data maps

8. maps / data maps

9. ISOTYPE – Otto and Marie Neurath

10. ISOTYPE – Otto and Marie Neurath

11. maps / data maps

12. wayfinding

13. tower graphics

14. tower graphics
http://www.angelamorelli.com/water/
Angela Morelli

15. diagrams

16. diagrams

17. diagrams

18. diagrams

19. Sankey diagrams

20. Sankey diagrams

21. Sankey diagrams
raw.densitydesign.org

22. motion graphics
Röysopp - Remind Me (Music video)

23. interactives
http://drones.pitchinteractive.com

24. and other things?
http://infosthetics.com http://www.informationisbeautiful.net
http://www.visualcomplexity.com/vc/ http://www.densitydesign.org/blog/

25. why visualise data?

26. efficient
communication?
engagement
with audience?
emotive
connection?
to explain
complex
information?condensing into
a small space?
enabling
comparisons?
quick overview?
selective
focussing?

27. exploration
explanation

28. production methods

29. 1. Use standard graph tools
2. Use existing specialist data
visualisation tools
3. Do it by hand
4. Develop new specialist data
visualisation tools

30. 1. Use standard graph tools

31. Open Office / Microsoft Excel / Apple Numbers

32. SPSS / Stata / R

33. https://infogr.am

34. 2. Use existing specialist data
visualisation tools

35. http://raw.densitydesign.org

36. IncomeperPersonoftheWorld
Life Expectancy of the World
Liechtenstein
Antigua
&Barbuda
Dominica
Palau
Nauru
Tuvalu
Seychelles
St. Kitts
& Nevis
Andorra
San Marino
Monaco
Andorra
St.Lucia
Panama
Sao Tome
and Principe
Tonga
Samoa
Grenada
Brunei
Comoros
Djibouti
Equatorial Guinea
Gabon
Luxembourg
Namibia
Swaziland
Timor-Leste
Micronesia
Trinidad and Tobago
Albania
Bhutan
Kiribati
Kosovo
Cyprus
Maldives
Slovenia
Suriname
Belize
Mauritius
Bahamas
Malta
Vanuatu
Montenegro
Estonia
Gambia
Guinea-Bissau
Lesotho
Botswana
Mongolia
Oman
Qatar
Iceland
Barbados
BahrainCape
Verde
Latvia
Solomon
Islands
Macedonia
Fiji
Guyana
Jamaica
St.Vincent
and G.
Armenia
Lithuania
Uruguay
Mauritania
Moldova
Kuwait
Congo, Rep.
Liberia
Bosnia and H.
Croatia
Lebanon
Israel
Costa Rica
Puerto Rico
New Zealand
Georgia
Central African Rep.
Sweden
Singapore
Norway
Ireland
Finland
Austria
Turkmenistan
Slovak Rep.
Kyrgyzstan
Eritrea
DenmarkTaiwan
Papua New Guinea
Hong Kong
United Arab Emirates
South Sudan
Switzerland
Hungary
BelarusAzerbaijan
Dom.R.
Bulgaria
Serbia
Burundi
Libya
Nicaragua
Palestine
Sierra Leone
Laos
Benin
Guinea
Somalia
Tajikistan
Togo
El Salvador
Honduras Paraguay
Jordan
Poland
Bolivia
Haiti
Czech Rep.
Portugal
Tunisia
Rwanda
Greece
Belgium
Cuba
Chad
Senegal
Zimbabwe
Zambia
Cambodia
Ecuador
Guatemala
Burkina
Faso
Malawi
Niger
Mali
Kazakhstan
Netherlands
Chile
Romania
Cameroon
Sri Lanka
Cote d'Ivoire
Angola
Madagascar
Syria
Australia
Mozambique
Yemen
North Korea
Afghanistan
Ghana
Nepal
Sudan
Saudi
Arabia
Peru
Venezuela
Malaysia
Morocco
Uzbekistan
Italy
Spain
UK
Germany
Canada
France
South Korea
Philippines
Vietnam
Ethiopia
Egypt
Iran
Turkey
Dem. Rep. Congo
Thailand
South Africa
Myanmar
Colombia
Ukraine
Tanzania
Kenya
Argentina
Algeria
Iraq
Uganda
China
Bangladesh
Indonesia
Pakistan
USA
Russia
Brazil
Nigeria
Japan
Mexico
India
2011 data for all 193 UN Members and for
Hong Kong, Kosovo, Palestine, Puerto
Rico and Taiwan.
Documentation and
pdf version for print at:
3 or
less
10
100
1000
millions
Colour by region
Size by population
If you want to see more data visit:
www.gapminder.org
Free to copy, share and
remix, but attribute to
Gapminder Foundation.
Version 11 September 2012
maplayoutbyPaoloFausone
gapminder.org/downloads/world-pdf
GAPMINDER WORLD 2012
Mapping the Wealth and Health of Nations
Healthy
Poor Rich
Sick
50
500 1 000 2 000 20 0005 000 10 000 50 000
60
70
80
55
65
75
income per person in US Dollars (GDP/capita, PPP$ inflation adjusted, log scale)
lifeexpectancyinyears
Gapminder - Hans Rosling

37. eSankey!

38. http://www.tableausoftware.com/

39. http://www.tableausoftware.com/

40. 3. Do it by hand
(or work with a specialist)

43. Software:
Presentation software
Illustrator
InDesign
Premiere
After effects
Animate
Processing / D3
HTML5 / Javascript
Other programming languages
Stills
{
Motion
}
Interactive
{

44. Processing
http://processing.org/

45. http://d3js.org/

46. 4. Develop new specialist data
visualisation tools

47. 55 75 95
ADAS-cog
MMSE
SIB
other
ADCS-ADL
DAD
PDS
other
NPI
other
CIBIC
GDS
CDR
ADCS-CGIC
QoL
author ageslocation
design, size
& follow-up
study
quality
cog
0yr 1 2
no. of
centres
0 10 20 30
baseline
MMSE sex
outcome measures used
func be glo
55 75 95
ADAS-cog
MMSE
SIB
other
ADCS-ADL
DAD
PDS
other
NPI
other
CIBIC
GDS
CDR
ADCS-CGIC
QoL
0yr 1 2 0 10 20 30 cog func be glo
N=161
Donepezil1mgN=42 M F
Rand
Char
Blind
Analy
N=473
M F
M F
Rand
Char
Blind
Analy
M F
M F
Rand
Char
Blind
Analy
Rogers etal.
1998 (B)
Rogers &
1996
?
Donepezil3mgN=40
Donepezil5mgN=39
Placebo N=40
Donepezil5mg N=154
Placebo N= 162
N=468
Donepezil5mg N=157
Placebo N= 153
M F
M F
M F
Rogers etal.
1998 (A)
Donepezil10mgN=157
M F
Donepezil10mgN=158
M F
M F
M F
Rand
Char
Blind
Analy
M F
M F
Rand
Char
Blind
Analy
M F
M F
Rand
Char
Blind
Analy
M F
M F
Rand
Char
Blind
Analy
N=818
Donepezil5mg N=271
Placebo N= 274
N=60
Donepezil5mg (D)
Placebo (p)
N=268
Donepezil5mg N=134
Placebo N= 129
N=431
Donepezil10mgN=214
Placebo N= 217
Burns etal.
1999
Greenberg
et al.
2000
Hommaetal.
2000
Mohs etal.
2001
Donepezil10mgN=273
M F
group1(p-D-p-p)N=30
group2(p-p-D-p)N=30
1mg
3mg
5mg
5mg
10
mg
5mg
10
mg
5mg
10mg

48. Title
Able Baker et al. 2010
Dogg's Hamlet 2009
Echo Bazaar 2006
Gender
M F
M F
M F
M F
Ages Sites
?
Features Outcomes
N=50
N=150
N=25
Design
Control N=28
500mg N=22

49. Programming / scripting languages
Processing
d3
javascript
python
etc...

50. 1. Use standard graph tools
2. Use existing specialist data
visualisation tools
3. Do it by hand
4. Develop new specialist data
visualisation tools

51. make physical graphics
secret no. 5.

52. sugarstacks.com

53. Stan’s Cafe: stanscafe.co.uk

55. Dr Will Stahl-Timmins
blog.willstahl.com
www.bmj.com/infographics
Twitter: @will_s_t
www.bmj.com/infographics