08 visualisation seminar ver0.2
TRANSCRIPT
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9GoldenRulesofDataVisualisa2on
Dr.BrianMacNamee
Retinal Variables
The Semiology of Graphics: Diagrams, Networks, Maps Jacques Bertin (1967)
Orientation
Color
Shape
Texture
Value
Size
Matplotlib Visualisation Gallery http://matplotlib.org/gallery.html
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NASA/Goddard Space Flight Center and the Advanced Visualization Laboratory at the National Center for Supercomputing Applications, B. Robertson, L. Hernquist
Peopleareverygoodatpa9erndetec2on1
Focusonthetaskathand2
Understandvisualpercep2on3
Priori2zefunc2onoverform4
Showallthedataandonlythedata5
Thinkcarefullyaboutcolour6
Thinkaboutthestoryyourdataistelling7
Knowyouraudience8
Trytocombinechartsandnumbers9
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“If you're going underground, why
do you need bother about geography? … Connections are
the thing”
Harry Beck
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UnderstandVisualPercep2on
3
http://www.statista.com/chart/3199/the-most-successful-teams-in-the-six-nations/
1,098
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France' England' Wales' Ireland' Italy' Scotland'
1,098
Points Scored
2,061&1,899& 1,833&
1,688&
1,183& 1,098&
England& France& Ireland& Wales& Italy& Scotland&
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Datavisualisa9onisaboutmappingdatadimensionstovisualencodings
Team Points
England 2,061
France 1,899
Ireland 1,833
Wales 1,688
Italy 1,183
Scotland 1,098
England'
France'
Ireland'
Wales'
Italy'
Scotland'
France'
Wales'
Scotland'
England'
Ireland'
Italy'
2,061&1,899& 1,833&
1,688&
1,183& 1,098&
England& France& Ireland& Wales& Italy& Scotland&
Jacques Bertin (1918 - 2010)
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Retinal Variables
The Semiology of Graphics: Diagrams, Networks, Maps Jacques Bertin (1967)
Orientation
Color
Shape
Texture
Value
Size
William Cleveland
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Position
Length
Angle
Area
Colour Density
Interpretation Accuracy
Pattern
WhichSegmentIsBiggest?
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“Be clear first and clever second. If you have to throw one of those out,
throw out clever.”
Jason Fried
“Be clear first and clever second. If you have to throw one of those out,
throw out clever.”
Jason Fried
Or: If your solution is not based on
bar charts and line charts, think carefully!
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-
100,000
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Independents FineGael FineFail SinnFein Labour
VotesbyPoli2calParty2014LocalElec2on
Independents
FineGael
FineFail
SinnFein
Labour
Removethecolourfromthebackground
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Independents FineGael FineFail SinnFein Labour
VotesbyPoli2calParty2014LocalElec2on
Independents
FineGael
FineFail
SinnFein
Labour
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Removethecolourfromthechartbackground
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Independents FineGael FineFail SinnFein Labour
VotesbyPoli2calParty2014LocalElec2on
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Labour
Removethelegend
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Independents FineGael FineFail SinnFein Labour
VotesbyPoli2calParty2014LocalElec2on
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Changetoabetter monochrome colour palette
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Independents FineGael FineFail SinnFein Labour
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Removethegridlines
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Independents FineGael FineFail SinnFein Labour
VotesbyPoli2calParty2014LocalElec2on
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Removethever2calaxisbutaddindatalabels
479,267
404,173427,644
256,314
121,898
Independents FineGael FineFail SinnFein Labour
VotesbyPoli2calParty2014LocalElec2on
Removethechartborder
479,267
404,173427,644
256,314
121,898
Independents FineGael FineFail SinnFein Labour
VotesbyPoli2calParty2014LocalElec2on
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479,267
404,173427,644
256,314
121,898
Independents FineGael FineFail SinnFein Labour
VotesbyPoli2calParty2014LocalElec2on
Wehaveimprovedthedata-ink ratio and in doing so have vastly improved our visualisation
479,267
404,173 427,644
256,314
121,898
Independents FineGael FineFail SinnFein Labour
VotesbyPoli2calParty2014LocalElec2on
-100,000200,000300,000400,000500,000600,000
VotesbyPoli2calParty2014LocalElec2on
Independents
FineGael
FineFail
SinnFein
Labour
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* Andrew Freedman, NOAA: 2012 Hottest & 2nd-Most Extreme Year On Record, Climate Centralhttp://www.climatecentral.org/news/noaa-2012-was-warmest-and-second-most-extreme-year-on-record-15436
* Sophie J. Engle, How to Lie with Data Visualization (in R) http://sjengle.cs.usfca.edu/talks/how-to-lie-with-data-visualization-in-r
* Sophie J. Engle, How to Lie with Data Visualization (in R) http://sjengle.cs.usfca.edu/talks/how-to-lie-with-data-visualization-in-r
http://www.climatecentral.org/news/noaa-2012-was-warmest-and-second-most-extreme-year-on-record-15436
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ThinkCarefullyAboutColour
6
For more great illusion examples take a look at: http://web.mit.edu/persci/gaz/
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Available here: http://www.lottolab.org/Visual%20Demos/Demo%2015.html
Available here: http://www.lottolab.org/Visual%20Demos/Demo%2015.html
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Cynthia Brewer
“[The RGB cube] is not the least bit perceptually scaled. In some parts of the cube a tiny step gives you a huge perceptual difference. In other parts it all looks the same.”
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Edward Tufte
“... avoiding catastrophe becomes the first principle in bringing color to information: above all, do no harm.”
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When using colour with numeric data use
monochromatic palettes
When using colour with categorical data use colour blindness safe palletes
Use colour palettes from ColorBrewer (www.colorbrewer2.org)
ThinkAboutTheStoryYourData
IsTelling
7
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26# 27# 28#
23#20#
15# 16#
Monday# Tuesday# Wednesday# Thursday# Friday# Saturday# Sunday#
Web
$Traffic$
Day$
Julie Steele
"How do the people I'm presenting the data to absorb information the best?"
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TrytoCombineChartsandNumbers
9
x1 y1 10 8.04 8 6.95
13 7.58 9 8.81
11 8.33 14 9.96 6 7.24 4 4.26
12 10.84 7 4.82 5 5.68
x2 y2 10 9.14 8 8.14
13 8.74 9 8.77
11 9.26 14 8.10 6 6.13 4 3.1
12 9.13 7 7.26 5 4.74
x3 y3 10 7.46 8 6.77
13 12.74 9 7.11
11 7.81 14 8.84 6 6.08 4 5.39
12 8.15 7 6.42 5 5.73
x4 y4 8 6.58 8 5.76 8 7.71 8 8.84 8 8.47 8 7.04 8 5.25
19 12.5 8 5.56 8 7.91 8 6.89
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x1 y1 10 8.04 8 6.95
13 7.58 9 8.81
11 8.33 14 9.96 6 7.24 4 4.26
12 10.84 7 4.82 5 5.68
x2 y2 10 9.14 8 8.14
13 8.74 9 8.77
11 9.26 14 8.10 6 6.13 4 3.1
12 9.13 7 7.26 5 4.74
x3 y3 10 7.46 8 6.77
13 12.74 9 7.11
11 7.81 14 8.84 6 6.08 4 5.39
12 8.15 7 6.42 5 5.73
x4 y4 8 6.58 8 5.76 8 7.71 8 8.84 8 8.47 8 7.04 8 5.25
19 12.5 8 5.56 8 7.91 8 6.89
Foursetsofdatawiththesamecorrela9oncoefficientof0.816
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"a computer should make both
calculations and graphs"
Frank Anscombe (1918 - 2001)
Peopleareverygoodatpa9erndetec2on1
Focusonthetaskathand2
Understandvisualpercep2on3
Priori2zefunc2onoverform4
Showallthedataandonlythedata5
Thinkcarefullyaboutcolour6
Thinkaboutthestoryyourdataistelling7
Knowyouraudience8
Trytocombinechartsandnumbers9
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Data Visualization Using Immersive Virtual Reality Tools Cioc, Alexandru; Djorgovski, S. G.; Donalek, C.; Lawler, E.; Sauer, F.; Longo, G.
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References
§ LondonUndergroundMap,HarryBeckwww.vam.ac.uk/vasta9c/microsites/1331_modernism/highlights_19.html
§ ColoursinCultures,DavidMcCandlesswww.informa9onisbeau9ful.net/visualiza9ons/colours-in-cultures/
§ OurIrresis9bleFascina9onwithAllThingsCircular,StephenFewwww.perceptualedge.com/ar9cles/visual_business_intelligence/our_fascina9on_with_all_things_circular.pdf
§ WhatAboutColorBlindness?,MaureenStonewww.stonesc.com/wordpress/2010/05/what-about-color-blindness/
§ Graphicalpercep9on:Theory,experimenta9on,andapplica9ontothedevelopmentofgraphicalmethods,WSCleveland,RMcGill,JournaloftheAmericanSta9s9calAssocia9onwww.researchgate.net/publica9on/229099907_Graphical_percep9on_Theory_experimenta9on_and_applica9on_to_the_development_of_graphical_methods
UseToolsThatMakeItEasyToDoThingsRight
9
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UsingVisualEncodingsEffec2vely§ Thesearesomethingsyoushouldthinkabouttohelpchoosingappropriatevisualencodings
– Usethemosteasilyinterpretedencodingforthemostimportantvariable
– Respectnaturalordering– Ensuresufficientdis9nctvalues– Choosedefaultsoverinnova9veformats– Takeaccountofreaders’context
HansRosling(Legend!)§ NowthatwehavetalkedaboutencodingstakealookatthisscreenshotfromHansRoslinganddiscusstheencodingsused
– Howmanydatadimensionsarevisuallyencoded?– Howisimportanceorderingused?
The tool and data that Rosling uses is available at: www.gapminder.org
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MakingDataCompelling
Year Temp 1895 50.54 1896 52.12 1897 51.58 1898 51.46 1899 51.10 1900 52.86 1901 51.94 1902 51.70