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Information visualization lecture 3

representation
Katrien Verbert
Department of Computer Science
Faculty of Science
Vrije Universiteit Brussel
katrien.verbert@vub.ac.be
06/03/14

pag. 1
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	
   y	
  =	
  3.00	
  +	
  0.500x	
  
for	
  each	
  data	
  set	
  
	
  

06/03/14

pag. 2
Ben	
  Shneiderman	
  	
  
hIp://www.youtube.com/watch?v=og7bzN0DhpI	
  
(watch	
  12:20	
  –	
  15:49	
  )	
  
06/03/14

pag. 3
Anscombe's quartet

06/03/14

pag. 4
Overview
•  Encoding of value
–  Univariate data
–  Bivariate data
–  Trivariate data
–  Hypervariate data
•  Encoding of relation
–  Lines
–  Maps and diagrams

06/03/14

pag. 5
Relations

30

MPG

Price £k

10 - 12

35

12 14
12 -- 14

40

16 - 18

Part of this car purchase interface identifies a relation
06/03/14

pag. 6
Relations

Interaction to identify a doctor highlights the hospital beds under his or her
care, and vice versa: an example of brushing
	
  
	
  

06/03/14

pag. 7
Overview
•  Encoding of value
–  Univariate data
–  Bivariate data
–  Trivariate data
–  Hypervariate data
•  Encoding of relation
–  Lines
–  Maps and diagrams

06/03/14

pag. 8
A single number

The	
  original	
  aircraX	
  al8meter,	
  responsible	
  for	
  many	
  accidents	
  
06/03/14

pag. 9
Representation of the view of an altimeter

06/03/14

pag. 10
An altimeter representation easily assumed to be
the same as shown on the previous slide

06/03/14

pag. 11
Change blindness

06/03/14

pag. 12
Change blindness

06/03/14

pag. 13
Change blindness

06/03/14

pag. 14
A modern aircraft altimeter

2200

2000

1820
00
1600
1400

stop
1200

06/03/14

pag. 15
Single number: second example

Source:	
  Image	
  by	
  kind	
  permission	
  of	
  Marcus	
  Watson	
  

06/03/14

pag. 16
A collection of numbers

Each	
  dot	
  represents	
  the	
  price	
  of	
  a	
  car	
  

06/03/14

pag. 17
Box plot
60

50
Price
(£K)
40

30

20

10

06/03/14

pag. 18
Box plot

06/03/14

pag. 19
histogram
8

6

4

2

1 –20

20–30

30–40

40–50

50–60

Price (£K)
06/03/14

pag. 20
bargram

Price £k

10 - 12

12 - 14

16 - 18

06/03/14

pag. 21
Bargram of categorical data

Nissan

Ford

Ferrari

MG

Cadillac

06/03/14

pag. 22
histogram of ordinal data

£200k	
  

£100k	
  

Monday	
  

Tuesday	
  

Wednesday	
   Thursday	
  

Friday	
  
06/03/14

pag. 23
Overview
•  Encoding of value
–  Univariate data
–  Bivariate data
–  Trivariate data
–  Hypervariate data
•  Encoding of relation
–  Lines
–  Maps and diagrams

06/03/14

pag. 24
Anscombe's quartet

06/03/14

pag. 25
Scatterplot

06/03/14

pag. 26
Time series

	
  
	
  
Android	
  Ac8va8ons	
  per	
  day,	
  measured	
  on	
  the	
  first	
  of	
  each	
  month	
  
06/03/14
	
  

pag. 27
Time series

	
  
	
  
Android	
  Ac8va8ons	
  per	
  day,	
  measured	
  on	
  the	
  first	
  of	
  each	
  month	
  
06/03/14
	
  

pag. 28
Stock data

06/03/14

pag. 29
time series

(a)	
  

(b)	
  

(c)	
  

(d)	
  

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	
  
pag. 30
Source:	
  Courtesy	
  of	
  Harry	
  Hochheiser	
  

06/03/14
Overview of the entire data set

06/03/14

pag. 31
time-box limits the display to items with prices
between $70 an $250 during days 1 to 4

06/03/14

pag. 32
additional constraint selects items with prices
between $70 and $95 during days 7 to 12

06/03/14

pag. 33
yet another constraint concerns prices
between $90 and $115 for days 15 to 18

06/03/14

pag. 34
Student activity meter

06/03/14

pag. 35
Time series

Representa8on	
  of	
  the	
  level	
  
of	
  ozone	
  concentra8on	
  
above	
  Los	
  Angeles	
  over	
  a	
  
period	
  of	
  ten	
  years	
  

06/03/14

pag. 36
Linked histogram

(a)

(b)

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. 37
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. 38
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. 39
Semantic zoom reveals data about a
second attribute
	
  

60

	
  
	
  

	
  

50

Price
	
  (£K)

40	
  
	
  

Ford	
  
Nissan	
  

VW	
  

40

35	
  
	
  

Merc	
  
Jag	
  
Jag	
  

30

3	
  0	
  

Ford	
  
SEAT	
  

	
  

20

	
  

10

06/03/14

pag. 40
Qualitative understanding of data

A	
  representa8on	
  of	
  Australia	
  and	
  New	
  Zealand	
  on	
  a	
  conven8onal	
  map	
  
06/03/14

pag. 41
Qualitative understanding of data

Australia

New
Zealand
A	
  representa8on	
  of	
  Australia	
  and	
  New	
  Zealand	
  indica8ng	
  that	
  some	
  aIribute	
  of	
  New	
  
pag. 42
Zealand	
  is	
  ten	
  8mes	
  its	
  value	
  for	
  Australia	
  
06/03/14
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)	
  
Overview
•  Encoding of value
–  Univariate data
–  Bivariate data
–  Trivariate data
–  Hypervariate data
•  Encoding of relation
–  Lines
–  Maps and diagrams

06/03/14

pag. 44
Does house A cost more than C?

D

Price

C
B

Bedrooms

A

Time

06/03/14

pag. 45
Scatterplot matrix

Bedrooms

D

A
B

Interac8on	
  can	
  offer	
  solu8on	
  
	
  
A	
  projec8on	
  of	
  the	
  data,	
  
allowing	
  comparison	
  of	
  Price	
  
and	
  Bedrooms	
  values	
  

C
Price

06/03/14

pag. 46
Scatterplot matrix

06/03/14

pag. 47
Cognitive overload? Interaction solution

The	
  highligh8ng	
  of	
  
houses	
  in	
  one	
  plane	
  is	
  
brushed	
  into	
  the	
  
remaining	
  planes	
  

06/03/14

pag. 48
Trivariate data
July ʻ97

Sept ʻ97

Nov ʻ97

Month
Jan ʻ98
of
Production
(MOP)
Mar ʻ98
May ʻ98

2

4
6
8
10
Months in service (MIS)

12

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	
  
pag. 49
06/03/14
indicates	
  the	
  number	
  of	
  faults	
  reported	
  for	
  a	
  given	
  MOP	
  and	
  MIS	
  
Trivariate data
Treble

Bass
	
  
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	
  06/03/14 pag. 50
Maps to represent trivariate data

A	
  representa8on	
  of	
  the	
  popula8on	
  of	
  major	
  ci8es	
  in	
  England,	
  Wales	
  and	
  Scotland.	
  Circle	
  area	
  is	
  
propor8onal	
  to	
  popula8on	
  
pag. 51
06/03/14
	
  
Also non-static representations of data

1900
1910
1920
1930
1940
1950
1960
1970
1980
1990
2000

Circles	
  change	
  in	
  size	
  as	
  the	
  decades	
  
are	
  animated,	
  so	
  that	
  sudden	
  changes	
  
in	
  popula8on	
  ‘pop	
  out’	
  	
  
06/03/14

pag. 52
hIp://www.youtube.com/watch?v=hVimVzgtD6w	
  
	
  
06/03/14

pag. 53
Overview
•  Encoding of value
–  Univariate data
–  Bivariate data
–  Trivariate data
–  Hypervariate data
•  Encoding of relation
–  Lines
–  Maps and diagrams

06/03/14

pag. 54
Simple scatterplot of bivariate data

Number
of
bedrooms

A
B
Price

	
  

A	
  simple	
  scaIerplot	
  represen8ng	
  the	
  price	
  and	
  number	
  of	
  bedrooms	
  associated	
  with	
  two	
  houses	
  
	
  
06/03/14

pag. 55
Price

Number
of
bedrooms

An	
  alterna8ve	
  representa8on	
  to	
  the	
  scaIerplot	
  in	
  which	
  the	
  two	
  aIribute	
  scales	
  are	
  presented	
  
in	
  parallel,	
  thereby	
  requiring	
  two	
  points	
  to	
  represent	
  each	
  house	
  
pag. 56
	
  
06/03/14
Labels

B
A
Price

Number
of
bedrooms

To	
  avoid	
  ambiguity	
  the	
  pair	
  of	
  points	
  represen8ng	
  a	
  house	
  are	
  joined	
  and	
  labelled	
  
pag. 57
06/03/14
	
  
Parallel coordinates

A

B

C

D

E

F

G

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	
  
pag. 58
06/03/14
between	
  B	
  and	
  E,	
  and	
  the	
  correla8on	
  between	
  C	
  and	
  G,	
  are	
  not	
  
Parallel coordinates

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	
  
pag. 59
Source:	
  Harri	
  Siirtola	
  

06/03/14
Student activity meter

06/03/14

pag. 60
Star plot
Mathematics
Sport

Chemistry

Physics

Literature

History
Art
Geography

06/03/14

pag. 61
Star plot for comparison

Bob’s	
  performance	
  

Tony’s	
  performance	
  
06/03/14

pag. 62
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	
  
Histogram

	
  
	
  
A	
  histogram	
  represen8ng	
  the	
  prices	
  of	
  a	
  collec8on	
  of	
  houses.	
  The	
  contribu8on	
  of	
  one	
  house	
  is	
  
pag. 65
06/03/14
shown	
  in	
  yellow	
  
Limits on Price identify a subset of
houses, coded green

06/03/14

pag. 66
Linked histograms

Houses	
  defined	
  by	
  the	
  limits	
  on	
  Price	
  are	
  coded	
  green	
  in	
  other	
  aIribute	
  histograms	
  

06/03/14

pag. 67
Linked histograms

	
  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	
  
pag. 68
06/03/14
green	
  if	
  the	
  the	
  limit	
  is	
  extended	
  to	
  include	
  it	
  
Linked histograms

Even	
  if	
  no	
  houses	
  sa8sfy	
  all	
  aIribute	
  limits,	
  black	
  houses,	
  which	
  fail	
  only	
  one	
  limit,	
  provide	
  
pag. 69
06/03/14
guidance	
  as	
  to	
  the	
  effect	
  of	
  relaxing	
  limits	
  
Linked histograms

	
  
An	
  AIribute	
  Explorer	
  representa8on	
  of	
  three	
  dimensions	
  of	
  communica8on	
  data	
  captured	
  during
pag. 70
an	
  emergency	
  services	
  exercise,	
  suppor8ng	
  interac8ve	
  explora8on	
  by	
  an	
  analyst	
   06/03/14
Linked histogram

Details	
  in	
  lecture	
  6:	
  case	
  studies	
  

06/03/14

pag. 71
Details of the Titanic disaster

Class
Survived

No
Yes
No
Yes
No
Yes
No
Yes

Age

Gender

Adult

Male

Child
Adult
Child

Female

1st

2nd

3rd

Crew

118
57
0
5
4
140
0
1

154
14
0
11
13
80
0
13

387
75
35
13
89
76
17
14

670
192
0
0
3
20
0
0

06/03/14

pag. 72
Steps	
  to	
  create	
  
mosaic	
  plot	
  
325 285

706

885

First Second

2201

Third

Crew

(a)

(b)

Survived

Female

Female
Died

Survived

Male

Male
Died

Adult

[Friendly,	
  2000]	
  
Child

First Second

Third

(d)

Crew

First Second

Third

(c)

Crew
Mosaic plot

06/03/14

pag. 74
Friendly’s webslte

hIp://www.datavis.ca/gallery/	
  
	
  
pag. 75
06/03/14
Icons

Chernoff	
  Faces	
  allow	
  aIribute	
  values	
  to	
  be	
  encoded	
  in	
  the	
  features	
  of	
  cartoon	
  faces	
  
06/03/14
(Chernoff	
  1973)	
  

pag. 76
Michael	
  Porath	
  
Example
Information visualization: representation
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. 80
Multidimensional icons representing eight
attributes of a dwelling

house
£400,000
garage
central heating
four bedrooms
good repair
large garden
Victoria 15 mins

flat
£300,000
no garage
central heating
two bedrooms
poor repair
small garden
Victoria 20 mins

houseboat
£200,000
no garage
no central heating
three bedrooms
good repair
no garden
Victoria 15 mins

06/03/14

pag. 81
Object visibility: each object is represented
as a single and coherent visual entity

Representa8ons	
  suppor8ve	
  
of	
  object	
  visibility	
  

06/03/14

pag. 82
Infocanvas

06/03/14

pag. 83
Representa8ons	
  of	
  mul8-­‐aIribute	
  objects	
  suppor8ve	
  of	
  aIribute	
  visibility	
  06/03/14

pag. 84
Attribute correlation

06/03/14

pag. 85
Object correlation

06/03/14

pag. 86
Overview
•  Encoding of value
–  Univariate data
–  Bivariate data
–  Trivariate data
–  Hypervariate data
•  Encoding of relation
–  Lines
–  Maps and diagrams

06/03/14

pag. 87
Relation

Relation (n): a logical or natural association between two or
more things; relevance of one to another; connection.

06/03/14

pag. 88
A simple symbol indicates the relationship
of marriage

John
Smith

Mary
Robinson

06/03/14

pag. 89
Social networks

06/03/14

pag. 90
Lines indicate relationship

John

Stingy Bank

1930 Bentley

06/03/14

pag. 91
Arrows indicate unique unilateral
functional relations

X1
Y

X2
X3
y=f(x)	
  	
  

06/03/14

pag. 92
Colour indicates a relation

06/03/14

pag. 93
Picts
Northumbria
Mercia
West Saxon
South Saxon
Isle of Wight
Kent
Britons

550

600

650

700

Years AD

The	
  incidence	
  of	
  warfare	
  in	
  early	
  Anglo-­‐Saxon	
  England	
  between	
  550	
  AD	
  and	
  700	
  AD.	
  Red	
  
indicates	
  the	
  aggressor,	
  green	
  the	
  aIacked	
  

06/03/14

pag. 94
Lines
B

Originator

Receiver

A
C
I
B
F
G
I
B
K
G
K
C
D

H
L
M
E
H
I
B
M
B
B
E
J
C

B

A

K

M

E

G

C

I

D

L

E

K

F
J

G

M

A

H
F

(a)

J

C

L

I

D

H

(b)

(c)

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)	
  

06/03/14

pag. 95
Useful?	
  

(b)	
  

(a)	
  
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	
  

06/03/14

pag. 96
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	
  	
  
hIp://seekshreyas.com/beerviz/	
  
	
  
06/03/14

pag. 98
hIp://visualiza8on.geblogs.com/visualiza8on/network/	
  
	
  
06/03/14

pag. 99
Chord diagram

06/03/14

pag. 100
06/03/14

pag. 101
An ‘association’ style chart depicting the African
bombings

06/03/14

pag. 102
Part of a ‘timeline’ style chart depicting the
Kennedy assassination

	
  Source:	
  Courtesy	
  i2	
  Ltd.	
  
06/03/14

pag. 103
Sankey diagram

hIp://bost.ocks.org/mike/sankey/	
  
	
  
06/03/14

pag. 104
Remember this one?

06/03/14

pag. 105
Flow map diagram

Migration from Colorado, migration from Norway and Latvia, whisky exports from Scotland.

	
  
Verbeek,	
  K.,	
  Buchin,	
  K.,	
  &	
  Speckmann,	
  B.	
  (2011).	
  Flow	
  map	
  layout	
  via	
  spiral	
  trees.	
  IEEE	
  
06/03/14
transac8ons	
  on	
  visualiza8on	
  and	
  computer	
  graphics,	
  17(12),	
  2536-­‐2544.	
  

pag. 106
Most familiar use of lines?

Harry	
  Beck’s	
  original	
  London	
  Underground	
  map	
  
Source:	
  ©	
  Transport	
  for	
  London	
  

06/03/14

pag. 107
The Underground map in use prior to the
introduction of Harry Beck’s map

Differences?	
  
Easier	
  to	
  use?	
  

Source:	
  ©	
  Transport	
  for	
  London	
  

06/03/14

pag. 108
Journey time?

06/03/14

pag. 109
hIp://www.london-­‐tubemap.com/journey_8mes.php	
  
	
  
06/03/14

pag. 110
hIp://www.tom-­‐carden.co.uk/p5/tube_map_travel_8mes/applet/	
  
	
  
06/03/14

pag. 111
Social networks

	
  
The	
  social	
  choices	
  of	
  fourth	
  grade	
  students	
  (aXer	
  Moreno,	
  1934)	
  
	
  

06/03/14

pag. 112
(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	
  
Overview
•  Encoding of value
–  Univariate data
–  Bivariate data
–  Trivariate data
–  Hypervariate data
•  Encoding of relation
–  Lines
–  Maps and diagrams

06/03/14

pag. 114
Maps and diagrams
Swimming
Pool

Hotels

Golf
Course

Restaurant

A
B
C
D
E
F
G
Facili8es	
  offered	
  by	
  eight	
  hotels	
  
06/03/14

pag. 115
Venn diagram

Swimming
pool

B

D

Golf

F
A

C

E
G
Restaurant

06/03/14

pag. 116
A Venn diagram representation of the
attributes of 24 hotels

Swimming
pool

Figure	
  3.83	
  

Golf

Restaurant
06/03/14

pag. 117
InfoCrystal

Price

*
Number of
bedrooms

Garden
size

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	
  
06/03/14 pag. 118
not	
  number	
  of	
  bedrooms	
  	
  
An Infocrystal representation of the
hotel data
Swimming
Pool

Golf

5

2
0

4

4
1

8
Restaurant
06/03/14

pag. 119
Cluster map

06/03/14

pag. 120
Cluster map

A	
  cluster	
  map	
  representa8on	
  of	
  	
  24	
  hotels,	
  each	
  described	
  by	
  four	
  aIributes	
  
Source:	
  Courtesy	
  ChrisLaan	
  Fluit,	
  Aduna	
  

06/03/14

pag. 121
TalkExplorer

Details	
  in	
  lecture	
  6:	
  case	
  studies	
  

06/03/14

pag. 122
Tree representations
designated root node
parent of A

sibling of A

A
leaf nodes
child of A

leaf nodes
06/03/14

pag. 123
Tree visualizations
hIp://www.informa8k.uni-­‐koeln.de/
ls_juenger/research/vbctool/	
  

	
  

Problems?	
  
06/03/14

pag. 124
Alternative: cone trees

(a)
(b)

(a)	
  A	
  tree	
  	
  (b)	
  The	
  corresponding	
  cone	
  tree	
  

06/03/14

pag. 125
Cam tree:
horizontal orientation of cone tree

06/03/14

pag. 126
Construction of a Tree Map
The	
  Tree	
  

Forma8on	
  of	
  the	
  
Tree	
  Map	
  
The	
  Tree	
  Map	
  

06/03/14

pag. 127
Slide and dice construction

Tree

Tree Map

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	
  
	
  
06/03/14 pag. 128
	
  
Tree map display of an author’s collection
of reports

Source:	
  Courtesy	
  of	
  Ben	
  Shneiderman	
  

06/03/14

pag. 129
Map of the market

hIp://www.marketwatch.com/tools/stockresearch/marketmap	
  
	
  

06/03/14

pag. 130
hIp://www.hivegroup.com/solu8ons/demos/usda.html	
  
	
  
06/03/14

pag. 131
hIp://www.ny8mes.com/interac8ve/2008/05/03/business/20080403_SPENDING_GRAPHIC.html?_r=0	
  
Ben Sheiderman on tree maps

	
  
	
  
hIp://www.youtube.com/watch?v=og7bzN0DhpI	
  

06/03/14

pag. 133
Tree map
pros and cons
Pros?

Cons?

06/03/14

pag. 134
Tree map
pros and cons
Pros

Cons

Color + Area
(2 attributes)

Hierarchy/Structure
hard to convey
aspect ratios

Slide	
  adapted	
  from	
  Michael	
  Porath	
  	
  

06/03/14

pag. 135
Aspect ratios

Which	
  one	
  is	
  bigger?	
  

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?	
  

make	
  the	
  segments	
  more	
  square!	
  
	
  
Slide	
  adapted	
  from	
  Michael	
  Porath	
  	
  

06/03/14

pag. 138
Layout Strategies / Algorithms

Cluster	
  

Squarified	
  

Pivot	
  By	
  Middle	
  

StripTreemap	
  

Pivot	
  By	
  Size	
  
hIp://hcil2.cs.umd.edu/trs/2001-­‐06/2001-­‐06.html	
  

Slide	
  adapted	
  from	
  Michael	
  Porath	
  	
  

	
  

06/03/14

pag. 139
Sunburst

hIp://bl.ocks.org/mbostock/4063423	
  
06/03/14
	
  

pag. 140
 

hIp://www.theguardian.com/news/datablog/2012/oct/05/beatles-­‐charts-­‐infographics	
  
hIp://hci.stanford.edu/jheer/files/zoo/	
  
	
  
06/03/14

pag. 142
Hyperbolic tree

	
  
A	
  sketch	
  illustra8on	
  of	
  the	
  hyperbolic	
  browser	
  representa8on	
  of	
  a	
  tree.	
  The	
  further	
  away	
  a	
  node	
  is	
  from	
  the	
  
06/03/14 pag. 143
root	
  node,	
  the	
  closer	
  it	
  is	
  to	
  its	
  superordinate	
  node,	
  and	
  the	
  area	
  it	
  occupies	
  decreases	
  
Nodes can typically be moved into center
position

	
  
(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	
  
06/03/14 pag. 144
	
  
Representa8on	
  of	
  the	
  Library	
  of	
  Congress	
  by	
  the	
  hyperbolic	
  browser	
  
hIp://philogb.github.io/jit/sta8c/v20/Jit/
Examples/Hypertree/example1.html	
  
	
  
hIp://www.autodeskresearch.com/projects/orgorgchart	
  
	
  
Readings
Chapter 3

06/03/14

pag. 148
Questions?

06/03/14

pag. 149
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 kdimensional space graphically. Journal of the American
Statistical Association, 68(342), 361-368.

06/03/14

pag. 150
project

06/03/14

pag. 151
Team project milestones
1. 
2. 
3. 
4. 
5. 

due	
  27	
  Feb.	
  
Form teams
due	
  13	
  March	
  
Project proposal
due	
  3	
  April	
  
Intermediate presentation
Final presentation
Short report

22	
  May	
  

due	
  29	
  May	
  

06/03/14

pag. 152
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. 153
Data collection
•  https://docs.google.com/forms/d/
1gHwVWHZLzWdSz1F37jA1Gungrl56bT215M6FYW3YqGY/
viewform
Or
•  bit.ly/N6JTyD

Anonymous! Choose your own ID.
•  Please report your data ;-)

06/03/14

pag. 154

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