This document contains summaries of various techniques for visually representing data with 1, 2, 3 or more variables. It begins with examples of encoding univariate data using techniques like bar graphs and histograms. It then discusses ways to encode bivariate data, such as scatterplots and linked histograms. Finally, it explores some options for visually encoding trivariate and higher-dimensional data, including bubble plots and maps. The document serves as an overview of fundamental data visualization concepts and techniques.

1. 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

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
y
=
3.00
+
0.500x
for
each
data
set
06/03/14
pag. 2

3. Ben
Shneiderman
hIp://www.youtube.com/watch?v=og7bzN0DhpI
(watch
12:20
–
15:49
)
06/03/14
pag. 3

4. Anscombe's quartet
06/03/14
pag. 4

5. Overview
• Encoding of value
– Univariate data
– Bivariate data
– Trivariate data
– Hypervariate data
• Encoding of relation
– Lines
– Maps and diagrams
06/03/14
pag. 5

6. 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

7. 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

8. Overview
• Encoding of value
– Univariate data
– Bivariate data
– Trivariate data
– Hypervariate data
• Encoding of relation
– Lines
– Maps and diagrams
06/03/14
pag. 8

9. A single number
The
original
aircraX
al8meter,
responsible
for
many
accidents
06/03/14
pag. 9

10. Representation of the view of an altimeter
06/03/14
pag. 10

11. An altimeter representation easily assumed to be
the same as shown on the previous slide
06/03/14
pag. 11

12. Change blindness
06/03/14
pag. 12

13. Change blindness
06/03/14
pag. 13

14. Change blindness
06/03/14
pag. 14

15. A modern aircraft altimeter
2200
2000
1820
00
1600
1400
stop
1200
06/03/14
pag. 15

16. Single number: second example
Source:
Image
by
kind
permission
of
Marcus
Watson
06/03/14
pag. 16

17. A collection of numbers
Each
dot
represents
the
price
of
a
car
06/03/14
pag. 17

18. Box plot
60
50
Price
(£K)
40
30
20
10
06/03/14
pag. 18

19. Box plot
06/03/14
pag. 19

20. histogram
8
6
4
2
1 –20
20–30
30–40
40–50
50–60
Price (£K)
06/03/14
pag. 20

21. bargram
Price £k
10 - 12
12 - 14
16 - 18
06/03/14
pag. 21

22. Bargram of categorical data
Nissan
Ford
Ferrari
MG
Cadillac
06/03/14
pag. 22

23. histogram of ordinal data
£200k
£100k
Monday
Tuesday
Wednesday
Thursday
Friday
06/03/14
pag. 23

24. Overview
• Encoding of value
– Univariate data
– Bivariate data
– Trivariate data
– Hypervariate data
• Encoding of relation
– Lines
– Maps and diagrams
06/03/14
pag. 24

25. Anscombe's quartet
06/03/14
pag. 25

26. Scatterplot
06/03/14
pag. 26

27. Time series
Android
Ac8va8ons
per
day,
measured
on
the
first
of
each
month
06/03/14
pag. 27

28. Time series
Android
Ac8va8ons
per
day,
measured
on
the
first
of
each
month
06/03/14
pag. 28

29. Stock data
06/03/14
pag. 29

30. 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

31. Overview of the entire data set
06/03/14
pag. 31

32. time-box limits the display to items with prices
between $70 an $250 during days 1 to 4
06/03/14
pag. 32

33. additional constraint selects items with prices
between $70 and $95 during days 7 to 12
06/03/14
pag. 33

34. yet another constraint concerns prices
between $90 and $115 for days 15 to 18
06/03/14
pag. 34

35. Student activity meter
06/03/14
pag. 35

36. Time series
Representa8on
of
the
level
of
ozone
concentra8on
above
Los
Angeles
over
a
period
of
ten
years
06/03/14
pag. 36

37. 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

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. 38

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. 39

40. 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

41. Qualitative understanding of data
A
representa8on
of
Australia
and
New
Zealand
on
a
conven8onal
map
06/03/14
pag. 41

42. 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

43. 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)

44. Overview
• Encoding of value
– Univariate data
– Bivariate data
– Trivariate data
– Hypervariate data
• Encoding of relation
– Lines
– Maps and diagrams
06/03/14
pag. 44

45. Does house A cost more than C?
D
Price
C
B
Bedrooms
A
Time
06/03/14
pag. 45

46. 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

47. Scatterplot matrix
06/03/14
pag. 47

48. Cognitive overload? Interaction solution
The
highligh8ng
of
houses
in
one
plane
is
brushed
into
the
remaining
planes
06/03/14
pag. 48

49. 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

50. 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

51. 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

52. 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

53. hIp://www.youtube.com/watch?v=hVimVzgtD6w
06/03/14
pag. 53

54. Overview
• Encoding of value
– Univariate data
– Bivariate data
– Trivariate data
– Hypervariate data
• Encoding of relation
– Lines
– Maps and diagrams
06/03/14
pag. 54

55. 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

56. 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

57. 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

58. 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

59. 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

60. Student activity meter
06/03/14
pag. 60

61. Star plot
Mathematics
Sport
Chemistry
Physics
Literature
History
Art
Geography
06/03/14
pag. 61

62. Star plot for comparison
Bob’s
performance
Tony’s
performance
06/03/14
pag. 62

63. 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

64. The
automa8c
display
of
addi8onal
detail
following
the
selec8on
of
narrower
limits
on
years
of
produc8on
and
film
length

65. 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

66. Limits on Price identify a subset of
houses, coded green
06/03/14
pag. 66

67. Linked histograms
Houses
defined
by
the
limits
on
Price
are
coded
green
in
other
aIribute
histograms
06/03/14
pag. 67

68. 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

69. 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

70. 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

71. Linked histogram
Details
in
lecture
6:
case
studies
06/03/14
pag. 71

72. 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

73. 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

74. Mosaic plot
06/03/14
pag. 74

75. Friendly’s webslte
hIp://www.datavis.ca/gallery/
pag. 75
06/03/14

76. Icons
Chernoff
Faces
allow
aIribute
values
to
be
encoded
in
the
features
of
cartoon
faces
06/03/14
(Chernoff
1973)
pag. 76

77. Michael
Porath

78. Example

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. 80

81. 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

82. Object visibility: each object is represented
as a single and coherent visual entity
Representa8ons
suppor8ve
of
object
visibility
06/03/14
pag. 82

83. Infocanvas
06/03/14
pag. 83

84. Representa8ons
of
mul8-­‐aIribute
objects
suppor8ve
of
aIribute
visibility
06/03/14
pag. 84

85. Attribute correlation
06/03/14
pag. 85

86. Object correlation
06/03/14
pag. 86

87. Overview
• Encoding of value
– Univariate data
– Bivariate data
– Trivariate data
– Hypervariate data
• Encoding of relation
– Lines
– Maps and diagrams
06/03/14
pag. 87

88. Relation
Relation (n): a logical or natural association between two or
more things; relevance of one to another; connection.
06/03/14
pag. 88

89. A simple symbol indicates the relationship
of marriage
John
Smith
Mary
Robinson
06/03/14
pag. 89

90. Social networks
06/03/14
pag. 90

91. Lines indicate relationship
John
Stingy Bank
1930 Bentley
06/03/14
pag. 91

92. Arrows indicate unique unilateral
functional relations
X1
Y
X2
X3
y=f(x)
06/03/14
pag. 92

93. Colour indicates a relation
06/03/14
pag. 93

94. 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

95. 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

96. 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

97. 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

98. hIp://seekshreyas.com/beerviz/
06/03/14
pag. 98

99. hIp://visualiza8on.geblogs.com/visualiza8on/network/
06/03/14
pag. 99

100. Chord diagram
06/03/14
pag. 100

101. 06/03/14
pag. 101

102. An ‘association’ style chart depicting the African
bombings
06/03/14
pag. 102

103. Part of a ‘timeline’ style chart depicting the
Kennedy assassination
Source:
Courtesy
i2
Ltd.
06/03/14
pag. 103

104. Sankey diagram
hIp://bost.ocks.org/mike/sankey/
06/03/14
pag. 104

105. Remember this one?
06/03/14
pag. 105

106. 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

107. Most familiar use of lines?
Harry
Beck’s
original
London
Underground
map
Source:
©
Transport
for
London
06/03/14
pag. 107

108. 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

109. Journey time?
06/03/14
pag. 109

110. hIp://www.london-­‐tubemap.com/journey_8mes.php
06/03/14
pag. 110

111. hIp://www.tom-­‐carden.co.uk/p5/tube_map_travel_8mes/applet/
06/03/14
pag. 111

112. Social networks
The
social
choices
of
fourth
grade
students
(aXer
Moreno,
1934)
06/03/14
pag. 112

113. (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

114. Overview
• Encoding of value
– Univariate data
– Bivariate data
– Trivariate data
– Hypervariate data
• Encoding of relation
– Lines
– Maps and diagrams
06/03/14
pag. 114

115. 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

116. Venn diagram
Swimming
pool
B
D
Golf
F
A
C
E
G
Restaurant
06/03/14
pag. 116

117. A Venn diagram representation of the
attributes of 24 hotels
Swimming
pool
Figure
3.83
Golf
Restaurant
06/03/14
pag. 117

118. 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

119. An Infocrystal representation of the
hotel data
Swimming
Pool
Golf
5
2
0
4
4
1
8
Restaurant
06/03/14
pag. 119

120. Cluster map
06/03/14
pag. 120

121. Cluster map
A
cluster
map
representa8on
of
24
hotels,
each
described
by
four
aIributes
Source:
Courtesy
ChrisLaan
Fluit,
Aduna
06/03/14
pag. 121

122. TalkExplorer
Details
in
lecture
6:
case
studies
06/03/14
pag. 122

123. Tree representations
designated root node
parent of A
sibling of A
A
leaf nodes
child of A
leaf nodes
06/03/14
pag. 123

124. Tree visualizations
hIp://www.informa8k.uni-­‐koeln.de/
ls_juenger/research/vbctool/
Problems?
06/03/14
pag. 124

125. Alternative: cone trees
(a)
(b)
(a)
A
tree
(b)
The
corresponding
cone
tree
06/03/14
pag. 125

126. Cam tree:
horizontal orientation of cone tree
06/03/14
pag. 126

127. Construction of a Tree Map
The
Tree
Forma8on
of
the
Tree
Map
The
Tree
Map
06/03/14
pag. 127

128. 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

129. Tree map display of an author’s collection
of reports
Source:
Courtesy
of
Ben
Shneiderman
06/03/14
pag. 129

130. Map of the market
hIp://www.marketwatch.com/tools/stockresearch/marketmap
06/03/14
pag. 130

131. hIp://www.hivegroup.com/solu8ons/demos/usda.html
06/03/14
pag. 131

132. hIp://www.ny8mes.com/interac8ve/2008/05/03/business/20080403_SPENDING_GRAPHIC.html?_r=0

133. Ben Sheiderman on tree maps
hIp://www.youtube.com/watch?v=og7bzN0DhpI
06/03/14
pag. 133

134. Tree map
pros and cons
Pros?
Cons?
06/03/14
pag. 134

135. 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

136. Aspect ratios
Which
one
is
bigger?
Slide
adapted
from
Michael
Porath
06/03/14
pag. 136

137. Aspect ratios
Which
one
is
bigger?
Slide
adapted
from
Michael
Porath
06/03/14
pag. 137

138. Aspect ratios
Which
one
is
bigger?
make
the
segments
more
square!
Slide
adapted
from
Michael
Porath
06/03/14
pag. 138

139. 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

140. Sunburst
hIp://bl.ocks.org/mbostock/4063423
06/03/14
pag. 140

141.
hIp://www.theguardian.com/news/datablog/2012/oct/05/beatles-­‐charts-­‐infographics

142. hIp://hci.stanford.edu/jheer/files/zoo/
06/03/14
pag. 142

143. 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

144. 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

145. Representa8on
of
the
Library
of
Congress
by
the
hyperbolic
browser

146. hIp://philogb.github.io/jit/sta8c/v20/Jit/
Examples/Hypertree/example1.html

147. hIp://www.autodeskresearch.com/projects/orgorgchart

148. Readings
Chapter 3
06/03/14
pag. 148

149. Questions?
06/03/14
pag. 149

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 kdimensional space graphically. Journal of the American
Statistical Association, 68(342), 361-368.
06/03/14
pag. 150

151. project
06/03/14
pag. 151

152. 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

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. 153

154. 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