Information visualization: representation

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Information about Information visualization: representation

Published on March 6, 2014

Author: kverbert


Information visualization lecture 3 representation Katrien Verbert Department of Computer Science Faculty of Science Vrije Universiteit Brussel 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://   (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://     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://     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  


Some criticism No evidence for pre-attentive nature [Morris et al. 1999] Src:  hIp://     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://     06/03/14 pag. 98

hIp://     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://     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://­‐     06/03/14 pag. 110

hIp://www.tom-­‐     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-­‐ 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://     06/03/14 pag. 130

hIp://     06/03/14 pag. 131


Ben Sheiderman on tree maps     hIp://   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://­‐06/2001-­‐06.html   Slide  adapted  from  Michael  Porath       06/03/14 pag. 139

Sunburst hIp://   06/03/14   pag. 140


hIp://     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:// Examples/Hypertree/example1.html    


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 • 1gHwVWHZLzWdSz1F37jA1Gungrl56bT215M6FYW3YqGY/ viewform Or • Anonymous! Choose your own ID. •  Please report your data ;-) 06/03/14 pag. 154

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