Information visualization: representation
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Transcript of Information visualization: representation
06/03/14 pag. 1
Information visualization lecture 3
representation
Katrien Verbert Department of Computer Science
Faculty of Science Vrije Universiteit Brussel
06/03/14 pag. 2
Anscombe's quartet
Property Value
Mean of x 9
Variance of x 11
Mean of y 7.50
Variance of y 4.122 or 4.127
Correla8on between x and y 0.816
Linear regression line for each data set
y = 3.00 + 0.500x
06/03/14 pag. 3
Ben Shneiderman hIp://www.youtube.com/watch?v=og7bzN0DhpI
(watch 12:20 – 15:49 )
06/03/14 pag. 4
Anscombe's quartet
06/03/14 pag. 5
Overview
• Encoding of value – Univariate data – Bivariate data – Trivariate data – Hypervariate data
• Encoding of relation – Lines – Maps and diagrams
06/03/14 pag. 6
Part of this car purchase interface identifies a relation
Relations
10 - 12 12 - 14 16 - 18£kPrice
MPG 30 35 40
12 - 14
06/03/14 pag. 7
Interaction to identify a doctor highlights the hospital beds under his or her care, and vice versa: an example of brushing
Relations
06/03/14 pag. 8
Overview
• Encoding of value – Univariate data – Bivariate data – Trivariate data – Hypervariate data
• Encoding of relation – Lines – Maps and diagrams
06/03/14 pag. 9
A single number
The original aircraX al8meter, responsible for many accidents
06/03/14 pag. 10
Representation of the view of an altimeter
06/03/14 pag. 11
An altimeter representation easily assumed to be the same as shown on the previous slide
06/03/14 pag. 12
Change blindness
06/03/14 pag. 13
Change blindness
06/03/14 pag. 14
Change blindness
06/03/14 pag. 15
2000
1600
2200
182000
stop1200
1400
A modern aircraft altimeter
06/03/14 pag. 16 Source: Image by kind permission of Marcus Watson
Single number: second example
06/03/14 pag. 17
Each dot represents the price of a car
A collection of numbers
06/03/14 pag. 18
60
50
40
30
20
10
Price (£K)
Box plot
06/03/14 pag. 19
Box plot
06/03/14 pag. 20
1 –20 20–30 30–40 40–50 50–60
Price (£K)
2
4
6
8
1 –20 20–30 30–40 40–50 50–60
Price (£K)
2
4
6
8
histogram
06/03/14 pag. 21
10 - 12 12 - 14 16 - 18£kPrice
bargram
06/03/14 pag. 22
Nissan Ford Ferrari MG Cadillac
Bargram of categorical data
06/03/14 pag. 23
Monday Tuesday Wednesday Thursday Friday
£100k
£200k
histogram of ordinal data
06/03/14 pag. 24
Overview
• Encoding of value – Univariate data – Bivariate data – Trivariate data – Hypervariate data
• Encoding of relation – Lines – Maps and diagrams
06/03/14 pag. 25
Anscombe's quartet
06/03/14 pag. 26
Scatterplot
06/03/14 pag. 27
Time series
Android Ac8va8ons per day, measured on the first of each month
06/03/14 pag. 28
Time series
Android Ac8va8ons per day, measured on the first of each month
06/03/14 pag. 29
Stock data
06/03/14 pag. 30
Four views of a 8me-‐series query tool. (a) An overview of the en8re data set; (b) a single 8me-‐box limits the display to items with prices between $70 an $250 during days 1 to 4; (c) an addi8onal constraint selects items with prices between $70 and $95 during days 7 to 12; (d) yet another constraint concerns prices between $90 and $115 for days 15 to 18 Source: Courtesy of Harry Hochheiser
(a) (b)
(c) (d)
time series
06/03/14 pag. 31
Overview of the entire data set
06/03/14 pag. 32
time-box limits the display to items with prices between $70 an $250 during days 1 to 4
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additional constraint selects items with prices between $70 and $95 during days 7 to 12
06/03/14 pag. 34
yet another constraint concerns prices between $90 and $115 for days 15 to 18
06/03/14 pag. 35
Student activity meter
06/03/14 pag. 36
Representa8on of the level of ozone concentra8on above Los Angeles over a period of ten years
Time series
06/03/14 pag. 37
Price Number ofbedrooms
(a)
(b)
(c)
(d)
imposed limits
imposed limits
Linked histogram
the price and number of bedrooms associated with a collection of houses are represented by separate histograms a single house is represented once on each histogram;
06/03/14 pag. 38
Linked histogram
upper and lower limits placed on Price define a subset of houses which are coded red on both histograms
06/03/14 pag. 39
Linked histogram
Interpretation is enhanced by ‘ranging down’ the colour-coded houses, especially if exploration involves the dynamic alteration of limits
06/03/14 pag. 40
60
50
20
10
Price (£K)
40
30
40
3 0
35
Ford
Nissan
VW Merc
Jag
Jag
Ford
SEAT
Semantic zoom reveals data about a second attribute
06/03/14 pag. 41
A representa8on of Australia and New Zealand on a conven8onal map
Qualitative understanding of data
06/03/14 pag. 42
A representa8on of Australia and New Zealand indica8ng that some aIribute of New Zealand is ten 8mes its value for Australia
Australia
NewZealand
Qualitative understanding of data
In the State of the World Atlas, magnifica8on encoding is used to give a first impression of popula8on densi8es. Note the reduced ‘size’ of Canada and Australia when compared with a conven8onal map Source: Smith (1999)
06/03/14 pag. 44
Overview
• Encoding of value – Univariate data – Bivariate data – Trivariate data – Hypervariate data
• Encoding of relation – Lines – Maps and diagrams
06/03/14 pag. 45
Price
Time
BedroomsAB
C
D
Does house A cost more than C?
06/03/14 pag. 46
A
B C
D
Price
Bedrooms
Scatterplot matrix
Interac8on can offer solu8on A projec8on of the data, allowing comparison of Price and Bedrooms values
06/03/14 pag. 47
Scatterplot matrix
06/03/14 pag. 48
The highligh8ng of houses in one plane is brushed into the remaining planes
Cognitive overload? Interaction solution
06/03/14 pag. 49
A representa8on of reported product failure, based on month of produc8on (MOP) of the failed product, and total months in service (MIS) before the fault occurred. The radius of each circle indicates the number of faults reported for a given MOP and MIS
MonthofProduction(MOP)
2 4 6 8 10 12Months in service (MIS)
July ʻ97
Sept ̒ 97
Nov ʻ97
Jan ʻ98
Mar ʻ98
May ʻ98
Trivariate data
06/03/14 pag. 50
Circles indicate the extent of the effect of a component on some property of the circuit, and change in size as the frequency cycles up and down the range from bass to treble
Treble
Bass
Trivariate data
06/03/14 pag. 51
A representa8on of the popula8on of major ci8es in England, Wales and Scotland. Circle area is propor8onal to popula8on
Maps to represent trivariate data
06/03/14 pag. 52
Circles change in size as the decades are animated, so that sudden changes in popula8on ‘pop out’
1900
1910
1920
1930
1940
1950
1960
1970
1980
1990
2000
Also non-static representations of data
06/03/14 pag. 53
hIp://www.youtube.com/watch?v=hVimVzgtD6w
06/03/14 pag. 54
Overview
• Encoding of value – Univariate data – Bivariate data – Trivariate data – Hypervariate data
• Encoding of relation – Lines – Maps and diagrams
06/03/14 pag. 55
A simple scaIerplot represen8ng the price and number of bedrooms associated with two houses
A
B
Price
Numberofbedrooms
Simple scatterplot of bivariate data
06/03/14 pag. 56
An alterna8ve representa8on to the scaIerplot in which the two aIribute scales are presented in parallel, thereby requiring two points to represent each house
Price Numberofbedrooms
06/03/14 pag. 57 To avoid ambiguity the pair of points represen8ng a house are joined and labelled
Price Numberofbedrooms
A
BLabels
06/03/14 pag. 58
A parallel coordinate plot for six objects, each characterised by seven aIributes. The trade-‐off between A and B, and the correla8on between B and C, are immediately apparent. The trade-‐off between B and E, and the correla8on between C and G, are not
A B C D E F G
Parallel coordinates
06/03/14 pag. 59
A parallel coordinate plot representa8on of a collec8on of cars, in which a range of the aIribute Year has been selected to cause all those cars manufactured during that period to be highlighted Source: Harri Siirtola
Parallel coordinates
06/03/14 pag. 60
Student activity meter
06/03/14 pag. 61
Sport
Literature
Mathematics
Physics
History
Geography
Art
Chemistry
Star plot
06/03/14 pag. 62
Bob’s performance Tony’s performance
Star plot for comparison
A scaIerplot enhanced by addi8onal and selec8ve encoding, allowing the selec8on of a film on the basis of type, dura8on, year of produc8on and other aIributes
The automa8c display of addi8onal detail following the selec8on of narrower limits on years of produc8on and film length
06/03/14 pag. 65
A histogram represen8ng the prices of a collec8on of houses. The contribu8on of one house is shown in yellow
Histogram
06/03/14 pag. 66
Limits on Price identify a subset of houses, coded green
06/03/14 pag. 67
Houses defined by the limits on Price are coded green in other aIribute histograms
Linked histograms
06/03/14 pag. 68
Green coding applies only to houses which sa8sfy all aIribute limits. Houses which fail one limit are coded black, so if a black house is posi8oned outside a limit it will turn green if the the limit is extended to include it
Linked histograms
06/03/14 pag. 69 Even if no houses sa8sfy all aIribute limits, black houses, which fail only one limit, provide guidance as to the effect of relaxing limits
Linked histograms
06/03/14 pag. 70
An AIribute Explorer representa8on of three dimensions of communica8on data captured during an emergency services exercise, suppor8ng interac8ve explora8on by an analyst
Linked histograms
06/03/14 pag. 71
Linked histogram
Details in lecture 6: case studies
06/03/14 pag. 72
Survived Age GenderClass
1st 2nd 3rd Crew
NoYesNoYesNoYesNoYes
Adult
Child
Adult
Child
Male
Female
118 57 0 5 4140 0 1
154 14 0 11 13 80 0 13
387 75 35 13 89 76 17 14
670192 0 0 3 20 0 0
Details of the Titanic disaster
2201 885706285325
First Second Third Crew
Female
Male
First Second Third Crew
Female
Male
Adult
First Second Third CrewChild
Survived
Died
Survived
Died
(a) (b)
(c)(d)
[Friendly, 2000]
Steps to create mosaic plot
06/03/14 pag. 74
Mosaic plot
06/03/14 pag. 75
Friendly’s webslte
hIp://www.datavis.ca/gallery/
06/03/14 pag. 76 Chernoff Faces allow aIribute values to be encoded in the features of cartoon faces (Chernoff 1973)
Icons
Michael Porath
Example
06/03/14 pag. 80
Some criticism
No evidence for pre-attentive nature [Morris et al. 1999]
Src: hIp://joshualedwell.typepad.com/usability_blog/files/final_vizualiza8on.pdf
06/03/14 pag. 81
Multidimensional icons representing eight attributes of a dwelling
house£400,000garagecentral heatingfour bedroomsgood repairlarge gardenVictoria 15 mins
flat£300,000no garagecentral heatingtwo bedroomspoor repairsmall gardenVictoria 20 mins
houseboat£200,000no garageno central heatingthree bedroomsgood repairno gardenVictoria 15 mins
06/03/14 pag. 82
Representa8ons suppor8ve of object visibility
Object visibility: each object is represented as a single and coherent visual entity
06/03/14 pag. 83
Infocanvas
06/03/14 pag. 84 Representa8ons of mul8-‐aIribute objects suppor8ve of aIribute visibility
06/03/14 pag. 85
Attribute correlation
06/03/14 pag. 86
Object correlation
06/03/14 pag. 87
Overview
• Encoding of value – Univariate data – Bivariate data – Trivariate data – Hypervariate data
• Encoding of relation – Lines – Maps and diagrams
06/03/14 pag. 88
Relation
Relation (n): a logical or natural association between two or more things; relevance of one to another; connection.
06/03/14 pag. 89
John Smith
MaryRobinson
A simple symbol indicates the relationship of marriage
06/03/14 pag. 90
Social networks
06/03/14 pag. 91
John Stingy Bank
1930 Bentley
Lines indicate relationship
06/03/14 pag. 92
YX1
X2
X3
Arrows indicate unique unilateral functional relations
y=f(x)
06/03/14 pag. 93
Colour indicates a relation
06/03/14 pag. 94
The incidence of warfare in early Anglo-‐Saxon England between 550 AD and 700 AD. Red indicates the aggressor, green the aIacked
PictsNorthumbriaMerciaWest SaxonSouth SaxonIsle of WightKentBritons
Years AD
550 600 650 700
06/03/14 pag. 95
Insight into even a short list of telephone calls (a) is enhanced by their node-‐link representa8on (b), especially if disconnected subsets can be iden8fied (c)
(a) (b) (c)
Originator Receiver
ACIBFGIBKGKCD
HLMEHIBMBBEJC
AB
C
D
E
F
GI
J
K
L
M
H
B
EK
G I
M
A H
FJ C
L D
Lines
06/03/14 pag. 96
A representa8on of mortgage ac8vity: (a) lenders, proper8es (houses), buyers, etc. are represented by small radial segments of an annulus as shown in (b), and their rela8onships denoted by straight lines
(a)
(b)
Useful?
A threshold has been imposed to suppress the display of normal behaviour. As a result, unusual behaviour is revealed by the paIerns formed by the lines
06/03/14 pag. 98
hIp://seekshreyas.com/beerviz/
06/03/14 pag. 99
hIp://visualiza8on.geblogs.com/visualiza8on/network/
06/03/14 pag. 100
Chord diagram
06/03/14 pag. 101
06/03/14 pag. 102
An ‘association’ style chart depicting the African bombings
06/03/14 pag. 103
Source: Courtesy i2 Ltd.
Part of a ‘timeline’ style chart depicting the Kennedy assassination
06/03/14 pag. 104
Sankey diagram
hIp://bost.ocks.org/mike/sankey/
06/03/14 pag. 105
Remember this one?
06/03/14 pag. 106
Flow map diagram
Verbeek, K., Buchin, K., & Speckmann, B. (2011). Flow map layout via spiral trees. IEEE transac8ons on visualiza8on and computer graphics, 17(12), 2536-‐2544.
Migration from Colorado, migration from Norway and Latvia, whisky exports from Scotland.
06/03/14 pag. 107 Harry Beck’s original London Underground map Source: © Transport for London
Most familiar use of lines?
06/03/14 pag. 108
Source: © Transport for London
The Underground map in use prior to the introduction of Harry Beck’s map
Differences? Easier to use?
06/03/14 pag. 109
Journey time?
06/03/14 pag. 110
hIp://www.london-‐tubemap.com/journey_8mes.php
06/03/14 pag. 111
hIp://www.tom-‐carden.co.uk/p5/tube_map_travel_8mes/applet/
06/03/14 pag. 112
The social choices of fourth grade students (aXer Moreno, 1934)
Social networks
(a) Social choices among department store employees (b) Social choices among department store employees, with marital status encoded (c) Social choices among department store employees, with age range encoded (blue <30, 30 <yellow <40, red >40) Source: L.C. Freeman
06/03/14 pag. 114
Overview
• Encoding of value – Univariate data – Bivariate data – Trivariate data – Hypervariate data
• Encoding of relation – Lines – Maps and diagrams
06/03/14 pag. 115
Facili8es offered by eight hotels
ABCDEFG
Swimming Pool
GolfCourse Restaurant
Hotels
Maps and diagrams
06/03/14 pag. 116
Swimming pool
Golf
Restaurant
A
B
C
D
E
F
G
Venn diagram
06/03/14 pag. 117
Figure 3.83
Swimming pool Golf
Restaurant
A Venn diagram representation of the attributes of 24 hotels
06/03/14 pag. 118
The development leading from a Venn diagram to an InfoCrystal. The InfoCrystal illustrated allows visual queries to be made concerning price, garden size and number of bedrooms. The asterisk represents houses sa8sfying criteria on Price and garden size but not number of bedrooms
Price
Garden size
Number of bedrooms
*
InfoCrystal
06/03/14 pag. 119
Swimming Pool
45
8
0
2
41
Golf
Restaurant
An Infocrystal representation of the hotel data
06/03/14 pag. 120
Cluster map
06/03/14 pag. 121 A cluster map representa8on of 24 hotels, each described by four aIributes Source: Courtesy ChrisLaan Fluit, Aduna
Cluster map
06/03/14 pag. 122
TalkExplorer
Details in lecture 6: case studies
06/03/14 pag. 123
designated root node
parent of A
sibling of A
child of A
leaf nodes
Aleaf nodes
Tree representations
06/03/14 pag. 124
Tree visualizations
hIp://www.informa8k.uni-‐koeln.de/ls_juenger/research/vbctool/
Problems?
06/03/14 pag. 125
(a) A tree (b) The corresponding cone tree
(a)
(b)
Alternative: cone trees
06/03/14 pag. 126
Cam tree: horizontal orientation of cone tree
06/03/14 pag. 127
The Tree
The Tree Map
Forma8on of the Tree Map
Construction of a Tree Map
06/03/14 pag. 128
The ‘slice-‐and-‐dice’ construc8on of a Tree Map to obtain leaf nodes represented by rectangles more suited to the inclusion of text and images
Tree
Tree Map
Slide and dice construction
06/03/14 pag. 129 Source: Courtesy of Ben Shneiderman
Tree map display of an author’s collection of reports
06/03/14 pag. 130
Map of the market hIp://www.marketwatch.com/tools/stockresearch/marketmap
06/03/14 pag. 131
hIp://www.hivegroup.com/solu8ons/demos/usda.html
hIp://www.ny8mes.com/interac8ve/2008/05/03/business/20080403_SPENDING_GRAPHIC.html?_r=0
06/03/14 pag. 133
hIp://www.youtube.com/watch?v=og7bzN0DhpI Watch 31:11 – 35:35
Ben Sheiderman on tree maps
06/03/14 pag. 134
Tree map pros and cons
Pros?
Cons?
06/03/14 pag. 135
Tree map pros and cons
Pros
Color + Area (2 attributes)
Cons
Hierarchy/Structure hard to convey
aspect ratios
Slide adapted from Michael Porath
06/03/14 pag. 136
Aspect ratios
Which one is bigger?
Slide adapted from Michael Porath
06/03/14 pag. 137
Aspect ratios
Which one is bigger?
Slide adapted from Michael Porath
06/03/14 pag. 138
Aspect ratios
Which one is bigger?
make the segments more square!
Slide adapted from Michael Porath
06/03/14 pag. 139
Layout Strategies / Algorithms
hIp://hcil2.cs.umd.edu/trs/2001-‐06/2001-‐06.html
Cluster Squarified StripTreemap
Pivot By Middle Pivot By Size
Slide adapted from Michael Porath
06/03/14 pag. 140
Sunburst
hIp://bl.ocks.org/mbostock/4063423
hIp://www.th
eguardian.com/new
s/datablog/2012/oct/05/beatle
s-‐charts-‐in
fographics
06/03/14 pag. 142
hIp://hci.stanford.edu/jheer/files/zoo/
06/03/14 pag. 143
A sketch illustra8on of the hyperbolic browser representa8on of a tree. The further away a node is from the root node, the closer it is to its superordinate node, and the area it occupies decreases
Hyperbolic tree
06/03/14 pag. 144
(a) The repor8ng structure of the employees of a company. (b) One employee of interest,
Rachel Anderson, has been moved towards the centre, revealing her subordinates
Nodes can typically be moved into center position
Representa8on of the Library of Congress by the hyperbolic browser
hIp://ph
ilogb.gith
ub.io/jit/sta8
c/v20/Jit/
Exam
ples/Hypertree/example1.htm
l
hIp://www.autod
eskresearch.com/projects/orgorgchart
06/03/14 pag. 148
Readings
Chapter 3
06/03/14 pag. 149
Questions?
06/03/14 pag. 150
References
• Christopher J. Morris, David S. Ebert, Penny Rheingans, An Experimental Analysis of the Pre-Attentiveness of Features in Chernoff Faces, Proceedings Applied Imagery Pattern Recognition, pp. 12–17, 1999.
• Friendly, Michael. Visualizing categorical data. SAS Institute, 2000.
• Chernoff, H. (1973). The use of faces to represent points in k-dimensional space graphically. Journal of the American Statistical Association, 68(342), 361-368.
06/03/14 pag. 151
project
06/03/14 pag. 152
Team project milestones
1. Form teams 2. Project proposal 3. Intermediate presentation 4. Final presentation 5. Short report
due 27 Feb.
due 13 March
due 3 April
22 May
due 29 May
06/03/14 pag. 153
Project proposal
1 page description of your intended project: – mo8va8on – which datasets you will use – current status. If available, first designs. – problems/ques8ons
due 13 March If you want earlier feedback, send us your proposal earlier ;-)
06/03/14 pag. 154
Data collection
• https://docs.google.com/forms/d/1gHwVWHZLzWdSz1F37jA1Gungrl56bT215M6FYW3YqGY/viewform Or
• bit.ly/N6JTyD
Anonymous! Choose your own ID.
• Please report your data ;-)