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Before we start maps Networks Références
Maps& Networks :
Why and How to Visualize ?
Christophe Bontemps
Toulouse School o...
Before we start maps Networks Références
MY JOB
Before we start maps Networks Références
WHAT IS DATA VISUALIZATION ?
Data visualisation serves at least two main purposes...
Before we start maps Networks Références
WHAT IS DATA VISUALIZATION ?
Data visualisation serves at least two main purposes...
Before we start maps Networks Références
WHAT IS DATA VISUALIZATION ?
Data visualisation serves at least two main purposes...
Before we start maps Networks Références
SUMMARY OF PREVIOUS EPISODES
From the perspective of the reader, “data visualisat...
Before we start maps Networks Références
SUMMARY OF PREVIOUS EPISODES
From the perspective of the reader, “data visualisat...
Before we start maps Networks Références
SUMMARY OF PREVIOUS EPISODES
From the perspective of the reader, “data visualisat...
Before we start maps Networks Références
THE BIG PICTURE
Before we start maps Networks Références
THE BIG PICTURE
Before we start maps Networks Références
THE BIG PICTURE
Before we start maps Networks Références
THE BIG PICTURE
Before we start maps Networks Références
THE BIG PICTURE
Before we start maps Networks Références
THE BIG PICTURE
Before we start maps Networks Références
THE BIG PICTURE
Before we start maps Networks Références
TO CITE A FEW
“All graphs are comparisons” ...
Before we start maps Networks Références
TO CITE A FEW
“All graphs are comparisons” ...
“All of statistics are comparisons...
Before we start maps Networks Références
TO CITE A FEW
“All graphs are comparisons” ...
“All of statistics are comparisons...
Before we start maps Networks Références
TO CITE A FEW
“All graphs are comparisons” ...
“All of statistics are comparisons...
Before we start maps Networks Références
TO CITE A FEW
“All graphs are comparisons” ...
“All of statistics are comparisons...
Before we start maps Networks Références
WHAT’S A MAP ?
Visual representation of geographical information and/or
spatial d...
Before we start maps Networks Références
WHAT’S A MAP ?
Visual representation of geographical information and/or
spatial d...
Before we start maps Networks Références
WHAT’S A MAP ?
Visual representation of geographical information and/or
spatial d...
Before we start maps Networks Références
WHAT’S A MAP ?
Visual representation of geographical information and/or
spatial d...
Before we start maps Networks Références
A CASE STUDY : ELECTIONS
Most of the election’s reports are maps : legitimate que...
Before we start maps Networks Références
A CASE STUDY : ELECTIONS
Most of the election’s reports are maps : legitimate que...
Before we start maps Networks Références
A CASE STUDY : ELECTIONS
Most of the election’s reports are maps : legitimate que...
Before we start maps Networks Références
A CASE STUDY : ELECTIONS
Most of the election’s reports are maps : legitimate que...
Before we start maps Networks Références
A CASE STUDY : ELECTIONS
2016 results as a map : Washington Post
Before we start maps Networks Références
A CASE STUDY : ELECTIONS
2012 results as a map : New York Time
Before we start maps Networks Références
A CASE STUDY : ELECTIONS
2008 results as a map : New York Time
Before we start maps Networks Références
A CASE STUDY : ELECTIONS
What’s wrong ? I cannot see anything ! I cannot answer
l...
Before we start maps Networks Références
A CASE STUDY : ELECTIONS
What’s wrong ? I cannot see anything ! I cannot answer
l...
Before we start maps Networks Références
A CASE STUDY : ELECTIONS
What’s wrong ? I cannot see anything ! I cannot answer
l...
Before we start maps Networks Références
A CASE STUDY : ELECTIONS
What’s wrong ? I cannot see anything ! I cannot answer
l...
Before we start maps Networks Références
A CASE STUDY : ELECTIONS
What’s wrong ? I cannot see anything ! I cannot answer
l...
Before we start maps Networks Références
A CASE STUDY : ELECTIONS
Maybe a better map ? From Financial Times blog
From Fina...
Before we start maps Networks Références
A CASE STUDY : ELECTIONS
2012 results as a map : New York Time
Before we start maps Networks Références
A CASE STUDY : ELECTIONS
Maybe spatial information is not the most relevant !
Before we start maps Networks Références
A CASE STUDY : ELECTIONS
Maybe spatial information is not the most relevant !
Before we start maps Networks Références
A CASE STUDY : ELECTIONS
Or if really geography matters (2004 campaign) :
From An...
Before we start maps Networks Références
GEOGRAPHY EXISTS IN THE DATA, BUT IS NOT THE
MATTER OF COMPARISON
Before we start maps Networks Références
ANOTHER GOOD EXAMPLE FOR NOT USING A MAP
From Our World in Data
Before we start maps Networks Références
EXAMPLES OF MAPS MISSISSIPPI MEANDERS
The interest here is to
compare location, l...
Before we start maps Networks Références
AIR MAP OF THE WORLD (1943)
Before we start maps Networks Références
THE LONDON COUNTY COUNCIL BOMB DAMAGE
MAPS, 1939-1945
Before we start maps Networks Références
AIR ROUTE MAPS : AARON KOBLIN
Before we start maps Networks Références
PARTIAL CONCLUSION
Maps are useful
Before we start maps Networks Références
PARTIAL CONCLUSION
Maps are useful
There are rules (over representation of large ...
Before we start maps Networks Références
PARTIAL CONCLUSION
Maps are useful
There are rules (over representation of large ...
Before we start maps Networks Références
PARTIAL CONCLUSION
Maps are useful
There are rules (over representation of large ...
Before we start maps Networks Références
PARTIAL CONCLUSION
Maps are useful
There are rules (over representation of large ...
Before we start maps Networks Références
PARTIAL CONCLUSION
Maps are useful
There are rules (over representation of large ...
Before we start maps Networks Références
PARTIAL CONCLUSION
Maps are useful
There are rules (over representation of large ...
Before we start maps Networks Références
QUESTIONS ON NETWORKS :
There are many challenges in statistical networks analysi...
Before we start maps Networks Références
QUESTIONS ON NETWORKS :
There are many challenges in statistical networks analysi...
Before we start maps Networks Références
QUESTIONS ON NETWORKS :
There are many challenges in statistical networks analysi...
Before we start maps Networks Références
QUESTIONS ON NETWORKS :
There are many challenges in statistical networks analysi...
Before we start maps Networks Références
IS THIS A NETWORK OR A MAP ?
Before we start maps Networks Références
IS THIS A NETWORK OR A MAP ?
This is a Map showing links (air connections) betwee...
Before we start maps Networks Références
IS THIS A NETWORK OR A MAP ?
This is a Map showing links (air connections) betwee...
Before we start maps Networks Références
TYPES OF NETWORKS
Adjacency Networks
Before we start maps Networks Références
TYPES OF NETWORKS
Adjacency Networks
Before we start maps Networks Références
TYPES OF NETWORKS
Adjacency Networks
Bipartite Networks
Before we start maps Networks Références
TYPES OF NETWORKS
Adjacency Networks
Bipartite Networks
Before we start maps Networks Références
TYPES OF NETWORKS
Adjacency Networks
Bipartite Networks
From bipartite, one can c...
Before we start maps Networks Références
TYPES OF NETWORKS
Adjacency Networks
Bipartite Networks
From bipartite, one can c...
Before we start maps Networks Références
TYPES OF NETWORKS
Adjacency Networks
Bipartite Networks
From bipartite, one can c...
Before we start maps Networks Références
TYPES OF NETWORKS
Undirected (A& B ’know each other’) and directed (A ’owe
money ...
Before we start maps Networks Références
TYPES OF NETWORKS
Undirected (A& B ’know each other’) and directed (A ’owe
money ...
Before we start maps Networks Références
TYPES OF NETWORKS
Undirected (A& B ’know each other’) and directed (A ’owe
money ...
Before we start maps Networks Références
FAMILIES OF GRAPHS
Before we start maps Networks Références
FAMILIES OF GRAPHS
Complete (top left) ; ring (top right) ; tree (bottom left) ; ...
Before we start maps Networks Références
FAMILIES OF GRAPHS
Complete (top left) ; ring (top right) ; tree (bottom left) ; ...
Before we start maps Networks Références
A CLASSICAL NETWORK EXAMPLE
Relationships of all of Victor Hugo’s characters of "...
Before we start maps Networks Références
A CLASSICAL NETWORK EXAMPLE
Relationships of all of Victor Hugo’s characters of "...
Before we start maps Networks Références
A CLASSICAL NETWORK EXAMPLE
Relationships of all of Victor Hugo’s characters of "...
Before we start maps Networks Références
A CLASSICAL NETWORK EXAMPLE
Relationships of all of Victor Hugo’s characters of "...
Before we start maps Networks Références
A CLASSICAL NETWORK EXAMPLE
Relationships of all of Victor Hugo’s characters of "...
Before we start maps Networks Références
A CLASSICAL NETWORK EXAMPLE
What do we see ? An overview of problems :
Not the sa...
Before we start maps Networks Références
A CLASSICAL NETWORK EXAMPLE
What do we see ? An overview of problems :
Not the sa...
Before we start maps Networks Références
A CLASSICAL NETWORK EXAMPLE
What do we see ? An overview of problems :
Not the sa...
Before we start maps Networks Références
A CLASSICAL NETWORK EXAMPLE
What do we see ? An overview of problems :
Not the sa...
Before we start maps Networks Références
A CLASSICAL NETWORK EXAMPLE
What do we see ? An overview of problems :
Not the sa...
Before we start maps Networks Références
HOW DOES IT WORK ?
A vast family of Force-directed placement network
representati...
Before we start maps Networks Références
HOW DOES IT WORK ?
A vast family of Force-directed placement network
representati...
Before we start maps Networks Références
HOW DOES IT WORK ?
A vast family of Force-directed placement network
representati...
Before we start maps Networks Références
HOW DOES IT WORK ?
A vast family of Force-directed placement network
representati...
Before we start maps Networks Références
HOW DOES IT WORK ?
A vast family of Force-directed placement network
representati...
Before we start maps Networks Références
HOW DOES IT WORK ?
A vast family of Force-directed placement network
representati...
Before we start maps Networks Références
HOW DOES IT WORK ?
A vast family of Force-directed placement network
representati...
Before we start maps Networks Références
FORCE-DIRECTED GRAPHS :
Physically-based model + aesthetic touch + fuzzy stuff......
Before we start maps Networks Références
RULES FOR “NODE-LINK” REPRESENTATIONS
Node-links representation follow rules :
Ge...
Before we start maps Networks Références
RULES FOR “NODE-LINK” REPRESENTATIONS
Node-links representation follow rules :
Ge...
Before we start maps Networks Références
RULES FOR “NODE-LINK” REPRESENTATIONS
Node-links representation follow rules :
Ge...
Before we start maps Networks Références
RULES FOR “NODE-LINK” REPRESENTATIONS
Node-links representation follow rules :
Ge...
Before we start maps Networks Références
RULES FOR “NODE-LINK” REPRESENTATIONS
Node-links representation follow rules :
Ge...
Before we start maps Networks Références
RULES FOR “NODE-LINK” REPRESENTATIONS
Node-links representation follow rules :
Ge...
Before we start maps Networks Références
RULES FOR “NODE-LINK” REPRESENTATIONS
Node-links representation follow rules :
Ge...
Before we start maps Networks Références
RULES FOR “NODE-LINK” REPRESENTATIONS
Node-links representation follow rules :
Ge...
Before we start maps Networks Références
LAYOUT AESTHETICS
From Katya Ognyanova (2015)
Before we start maps Networks Références
A NOTE ON DATA : MATRIX
From Katya Ognyanova (2015)
Before we start maps Networks Références
A NOTE ON DATA : EDGE LIST
From Katya Ognyanova (2015)
Before we start maps Networks Références
A NOTE ON DATA : NODE LIST
From Katya Ognyanova (2015)
Before we start maps Networks Références
A NOTE ON DATA : NODE ATTRIBUTES
From Katya Ognyanova (2015)
Before we start maps Networks Références
ALTERNATIVE VIEW : ADJACENT MATRIX PLOT
An adjacency matrix, where each cell ij r...
Before we start maps Networks Références
NETWORKS : ADJACENT MATRIX PLOT
As in many example, sorting is very useful !
Before we start maps Networks Références
NETWORKS : ADJACENT MATRIX PLOT
Adjacent matrix help identifying patterns
From Mc...
Before we start maps Networks Références
NETWORKS : ARC DIAGRAM PLOT
To put some structure in the graph : Horizontal align...
Before we start maps Networks Références
NETWORKS : ARC DIAGRAM PLOT
Now we can sort ! (according to groups, and then degr...
Before we start maps Networks Références
NETWORKS : ARC DIAGRAM PLOT : MY TOUCH !
Before we start maps Networks Références
NETWORKS : ROLE OF TOPOLOGY
Circular layouts of a 43-node, 80-edge network. Force...
Before we start maps Networks Références
NETWORKS : ADDING A TOPOLOGY HELPS
Circular layouts of a 43-node, 80-edge network...
Before we start maps Networks Références
NETWORKS : ADDING A TOPOLOGY HELPS
Circular layouts of a 43-node, 80-edge network...
Before we start maps Networks Références
PROBLEM 1 : IDENTIFY REFERENCE GRAPHS
Random Graphs difficult to identify
Before we start maps Networks Références
PROBLEM 1 : IDENTIFY REFERENCE GRAPHS
Random Graphs difficult to identify
Before we start maps Networks Références
PROBLEM 1 : IDENTIFY REFERENCE GRAPHS
Random Graphs difficult to identify
Or
Before we start maps Networks Références
PROBLEM 1 : IDENTIFY REFERENCE GRAPHS
Random Graphs difficult to identify
Or
Before we start maps Networks Références
PROBLEM 1 : IDENTIFY REFERENCE GRAPHS
Random Graphs difficult to identify
Or
→ How...
Before we start maps Networks Références
PROBLEM 2 : LAYOUT CHANGE PERCEPTION
Three layouts of the same graph
From Kolaczy...
Before we start maps Networks Références
PROBLEM 2 : LAYOUT CHANGE PERCEPTION
Three layouts of the same graph
From Kolaczy...
Before we start maps Networks Références
PROBLEM 2 : LAYOUT CHANGE PERCEPTION
Three layouts of the same graph
From Kolaczy...
Before we start maps Networks Références
PROBLEM 2 : LAYOUT CHANGE PERCEPTION
Three layouts of the same graph
→ Circular, ...
Before we start maps Networks Références
PROBLEM 3 : DECORATION HELPS ?
From Kolaczyk and Csárdi (2014)
Before we start maps Networks Références
PROBLEM 3 : DECORATION HELPS ?
Plain and decorated visualizations of a karate clu...
Before we start maps Networks Références
PROBLEM 4 : SCALABILITY ?
(192 blogs linked by 1,431 edges indicating at least on...
Before we start maps Networks Références
PROBLEM 4 : SCALABILITY ?
French political blog network
(192 blogs linked by 1,43...
Before we start maps Networks Références
PROBLEM 4 : SCALABILITY ?
French political blog network
Towards an "hairball" + c...
Before we start maps Networks Références
SOLUTIONS : SPLIT ?
From Kolaczyk and Csárdi (2014)
Before we start maps Networks Références
SOLUTIONS : SPLIT ?
Two ego-centric views of the karate club network
From Kolaczy...
Before we start maps Networks Références
SOLUTIONS : AGGREGATE ?
From Kolaczyk and Csárdi (2014)
Before we start maps Networks Références
SOLUTIONS : AGGREGATE ?
French political blog network at the level of political
p...
Before we start maps Networks Références
REFERENCES I
Bahoken, F., Beauguitte, L., and Lhomme, S. (2013). La visualisation...
Before we start maps Networks Références
REFERENCES II
McGuffin, M. J. (2012). Simple algorithms for network visualization ...
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Meetup Maps & Networks: Why and How to visualize

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Slides presented at the 2017, January 31st Toulouse Data Visualization Meetup on maps & Neworks.
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Meetup Maps & Networks: Why and How to visualize

  1. 1. Before we start maps Networks Références Maps& Networks : Why and How to Visualize ? Christophe Bontemps Toulouse School of Economics, INRA @Xtophe_Bontemps
  2. 2. Before we start maps Networks Références MY JOB
  3. 3. Before we start maps Networks Références WHAT IS DATA VISUALIZATION ? Data visualisation serves at least two main purposes : Data exploration Graphs as visual tests, comparisons (short time to built and to read)
  4. 4. Before we start maps Networks Références WHAT IS DATA VISUALIZATION ? Data visualisation serves at least two main purposes : Data exploration Graphs as visual tests, comparisons (short time to built and to read) Data representation Summaries, storytelling (long time to build, short time to read)
  5. 5. Before we start maps Networks Références WHAT IS DATA VISUALIZATION ? Data visualisation serves at least two main purposes : Data exploration Graphs as visual tests, comparisons (short time to built and to read) Data representation Summaries, storytelling (long time to build, short time to read) The problem is that : “ Communicating implies simplification data exploration implies exhaustivity”
  6. 6. Before we start maps Networks Références SUMMARY OF PREVIOUS EPISODES From the perspective of the reader, “data visualisation” are implicitly or explicitly comparisons or even tests (in the statistical sense). Graphics should help questioning
  7. 7. Before we start maps Networks Références SUMMARY OF PREVIOUS EPISODES From the perspective of the reader, “data visualisation” are implicitly or explicitly comparisons or even tests (in the statistical sense). Graphics should help questioning They should provide elements, to answer legitimate questions (or, at least show the data)
  8. 8. Before we start maps Networks Références SUMMARY OF PREVIOUS EPISODES From the perspective of the reader, “data visualisation” are implicitly or explicitly comparisons or even tests (in the statistical sense). Graphics should help questioning They should provide elements, to answer legitimate questions (or, at least show the data) If the question implies comparison, they should truthfully show the comparison
  9. 9. Before we start maps Networks Références THE BIG PICTURE
  10. 10. Before we start maps Networks Références THE BIG PICTURE
  11. 11. Before we start maps Networks Références THE BIG PICTURE
  12. 12. Before we start maps Networks Références THE BIG PICTURE
  13. 13. Before we start maps Networks Références THE BIG PICTURE
  14. 14. Before we start maps Networks Références THE BIG PICTURE
  15. 15. Before we start maps Networks Références THE BIG PICTURE
  16. 16. Before we start maps Networks Références TO CITE A FEW “All graphs are comparisons” ...
  17. 17. Before we start maps Networks Références TO CITE A FEW “All graphs are comparisons” ... “All of statistics are comparisons (too).” Gelman (2004)
  18. 18. Before we start maps Networks Références TO CITE A FEW “All graphs are comparisons” ... “All of statistics are comparisons (too).” Gelman (2004) “ The human eye acts is a broad feature detector and general statistical test”. Buja et al. (2009)
  19. 19. Before we start maps Networks Références TO CITE A FEW “All graphs are comparisons” ... “All of statistics are comparisons (too).” Gelman (2004) “ The human eye acts is a broad feature detector and general statistical test”. Buja et al. (2009) “There are no “good” nor “bad” graphics (...), there are graphics answering legitimate questions and graphics that do not answer question at all ” Bertin (1981)
  20. 20. Before we start maps Networks Références TO CITE A FEW “All graphs are comparisons” ... “All of statistics are comparisons (too).” Gelman (2004) “ The human eye acts is a broad feature detector and general statistical test”. Buja et al. (2009) “There are no “good” nor “bad” graphics (...), there are graphics answering legitimate questions and graphics that do not answer question at all ” Bertin (1981) “Above all, show the data” Tufte (2001)
  21. 21. Before we start maps Networks Références WHAT’S A MAP ? Visual representation of geographical information and/or spatial data A (statistical) map is a summary of data (to be compared with pictures)
  22. 22. Before we start maps Networks Références WHAT’S A MAP ? Visual representation of geographical information and/or spatial data A (statistical) map is a summary of data (to be compared with pictures) Many types of maps
  23. 23. Before we start maps Networks Références WHAT’S A MAP ? Visual representation of geographical information and/or spatial data A (statistical) map is a summary of data (to be compared with pictures) Many types of maps What question can ask to a Map ?
  24. 24. Before we start maps Networks Références WHAT’S A MAP ? Visual representation of geographical information and/or spatial data A (statistical) map is a summary of data (to be compared with pictures) Many types of maps What question can ask to a Map ? Many rules for drawing a map !
  25. 25. Before we start maps Networks Références A CASE STUDY : ELECTIONS Most of the election’s reports are maps : legitimate questions What happened ?
  26. 26. Before we start maps Networks Références A CASE STUDY : ELECTIONS Most of the election’s reports are maps : legitimate questions What happened ? How can I see the most important changes ?
  27. 27. Before we start maps Networks Références A CASE STUDY : ELECTIONS Most of the election’s reports are maps : legitimate questions What happened ? How can I see the most important changes ? Which are the states that vote the most for Clinton ? For Trump ?
  28. 28. Before we start maps Networks Références A CASE STUDY : ELECTIONS Most of the election’s reports are maps : legitimate questions What happened ? How can I see the most important changes ? Which are the states that vote the most for Clinton ? For Trump ? Can I rank the importance of each state in the outcome ?
  29. 29. Before we start maps Networks Références A CASE STUDY : ELECTIONS 2016 results as a map : Washington Post
  30. 30. Before we start maps Networks Références A CASE STUDY : ELECTIONS 2012 results as a map : New York Time
  31. 31. Before we start maps Networks Références A CASE STUDY : ELECTIONS 2008 results as a map : New York Time
  32. 32. Before we start maps Networks Références A CASE STUDY : ELECTIONS What’s wrong ? I cannot see anything ! I cannot answer legitimate questions ! Quantitative information is over represented in "large" states
  33. 33. Before we start maps Networks Références A CASE STUDY : ELECTIONS What’s wrong ? I cannot see anything ! I cannot answer legitimate questions ! Quantitative information is over represented in "large" states Easy to see the difference between states
  34. 34. Before we start maps Networks Références A CASE STUDY : ELECTIONS What’s wrong ? I cannot see anything ! I cannot answer legitimate questions ! Quantitative information is over represented in "large" states Easy to see the difference between states Easy to see the results for one state
  35. 35. Before we start maps Networks Références A CASE STUDY : ELECTIONS What’s wrong ? I cannot see anything ! I cannot answer legitimate questions ! Quantitative information is over represented in "large" states Easy to see the difference between states Easy to see the results for one state Easy to see "regions" of the same colour
  36. 36. Before we start maps Networks Références A CASE STUDY : ELECTIONS What’s wrong ? I cannot see anything ! I cannot answer legitimate questions ! Quantitative information is over represented in "large" states Easy to see the difference between states Easy to see the results for one state Easy to see "regions" of the same colour Only the "raw" elements are visible, not the details (limited questions)
  37. 37. Before we start maps Networks Références A CASE STUDY : ELECTIONS Maybe a better map ? From Financial Times blog From Financial Times
  38. 38. Before we start maps Networks Références A CASE STUDY : ELECTIONS 2012 results as a map : New York Time
  39. 39. Before we start maps Networks Références A CASE STUDY : ELECTIONS Maybe spatial information is not the most relevant !
  40. 40. Before we start maps Networks Références A CASE STUDY : ELECTIONS Maybe spatial information is not the most relevant !
  41. 41. Before we start maps Networks Références A CASE STUDY : ELECTIONS Or if really geography matters (2004 campaign) : From Andrew Gelman’s Blog. Each pixel represents 1,000 votes.
  42. 42. Before we start maps Networks Références GEOGRAPHY EXISTS IN THE DATA, BUT IS NOT THE MATTER OF COMPARISON
  43. 43. Before we start maps Networks Références ANOTHER GOOD EXAMPLE FOR NOT USING A MAP From Our World in Data
  44. 44. Before we start maps Networks Références EXAMPLES OF MAPS MISSISSIPPI MEANDERS The interest here is to compare location, lines,
  45. 45. Before we start maps Networks Références AIR MAP OF THE WORLD (1943)
  46. 46. Before we start maps Networks Références THE LONDON COUNTY COUNCIL BOMB DAMAGE MAPS, 1939-1945
  47. 47. Before we start maps Networks Références AIR ROUTE MAPS : AARON KOBLIN
  48. 48. Before we start maps Networks Références PARTIAL CONCLUSION Maps are useful
  49. 49. Before we start maps Networks Références PARTIAL CONCLUSION Maps are useful There are rules (over representation of large area, colorpleth,etc..)
  50. 50. Before we start maps Networks Références PARTIAL CONCLUSION Maps are useful There are rules (over representation of large area, colorpleth,etc..) Statistical maps are difficult to built :
  51. 51. Before we start maps Networks Références PARTIAL CONCLUSION Maps are useful There are rules (over representation of large area, colorpleth,etc..) Statistical maps are difficult to built : How to show uncertainty on a map ?
  52. 52. Before we start maps Networks Références PARTIAL CONCLUSION Maps are useful There are rules (over representation of large area, colorpleth,etc..) Statistical maps are difficult to built : How to show uncertainty on a map ? How to show correlations on a map ?
  53. 53. Before we start maps Networks Références PARTIAL CONCLUSION Maps are useful There are rules (over representation of large area, colorpleth,etc..) Statistical maps are difficult to built : How to show uncertainty on a map ? How to show correlations on a map ? Comparisons are limited (only geographical)
  54. 54. Before we start maps Networks Références PARTIAL CONCLUSION Maps are useful There are rules (over representation of large area, colorpleth,etc..) Statistical maps are difficult to built : How to show uncertainty on a map ? How to show correlations on a map ? Comparisons are limited (only geographical) Maps can help select, or highlight other elements (see D3.js, dashboards, or GeoXP package)
  55. 55. Before we start maps Networks Références QUESTIONS ON NETWORKS : There are many challenges in statistical networks analysis : Statistics on networks are quite difficult to handle
  56. 56. Before we start maps Networks Références QUESTIONS ON NETWORKS : There are many challenges in statistical networks analysis : Statistics on networks are quite difficult to handle Many types of networks
  57. 57. Before we start maps Networks Références QUESTIONS ON NETWORKS : There are many challenges in statistical networks analysis : Statistics on networks are quite difficult to handle Many types of networks Questions related to networks are hard to state (fuzzy or unarticulated)
  58. 58. Before we start maps Networks Références QUESTIONS ON NETWORKS : There are many challenges in statistical networks analysis : Statistics on networks are quite difficult to handle Many types of networks Questions related to networks are hard to state (fuzzy or unarticulated) Visualizations are barely readable (decoding is hard !)
  59. 59. Before we start maps Networks Références IS THIS A NETWORK OR A MAP ?
  60. 60. Before we start maps Networks Références IS THIS A NETWORK OR A MAP ? This is a Map showing links (air connections) between geographical entities.
  61. 61. Before we start maps Networks Références IS THIS A NETWORK OR A MAP ? This is a Map showing links (air connections) between geographical entities. This is “a collection of interconnected things”, (Oxford Englis Dictionary)
  62. 62. Before we start maps Networks Références TYPES OF NETWORKS Adjacency Networks
  63. 63. Before we start maps Networks Références TYPES OF NETWORKS Adjacency Networks
  64. 64. Before we start maps Networks Références TYPES OF NETWORKS Adjacency Networks Bipartite Networks
  65. 65. Before we start maps Networks Références TYPES OF NETWORKS Adjacency Networks Bipartite Networks
  66. 66. Before we start maps Networks Références TYPES OF NETWORKS Adjacency Networks Bipartite Networks From bipartite, one can create adjacency
  67. 67. Before we start maps Networks Références TYPES OF NETWORKS Adjacency Networks Bipartite Networks From bipartite, one can create adjacency
  68. 68. Before we start maps Networks Références TYPES OF NETWORKS Adjacency Networks Bipartite Networks From bipartite, one can create adjacency From Heijmans et al. (2014), and Kolaczyk (2009)
  69. 69. Before we start maps Networks Références TYPES OF NETWORKS Undirected (A& B ’know each other’) and directed (A ’owe money to’ B)
  70. 70. Before we start maps Networks Références TYPES OF NETWORKS Undirected (A& B ’know each other’) and directed (A ’owe money to’ B)
  71. 71. Before we start maps Networks Références TYPES OF NETWORKS Undirected (A& B ’know each other’) and directed (A ’owe money to’ B) From Kolaczyk and Csárdi (2014)
  72. 72. Before we start maps Networks Références FAMILIES OF GRAPHS
  73. 73. Before we start maps Networks Références FAMILIES OF GRAPHS Complete (top left) ; ring (top right) ; tree (bottom left) ; and star (bottom right)
  74. 74. Before we start maps Networks Références FAMILIES OF GRAPHS Complete (top left) ; ring (top right) ; tree (bottom left) ; and star (bottom right) From Kolaczyk and Csárdi (2014)
  75. 75. Before we start maps Networks Références A CLASSICAL NETWORK EXAMPLE Relationships of all of Victor Hugo’s characters of "Les Miserables". Data composed of nodes (characters) + Links (strength of links) http://bl.ocks.org/mbostock/4062045
  76. 76. Before we start maps Networks Références A CLASSICAL NETWORK EXAMPLE Relationships of all of Victor Hugo’s characters of "Les Miserables". Data composed of nodes (characters) + Links (strength of links) Nodes can be coloured (information on characters) http://bl.ocks.org/mbostock/4062045
  77. 77. Before we start maps Networks Références A CLASSICAL NETWORK EXAMPLE Relationships of all of Victor Hugo’s characters of "Les Miserables". Data composed of nodes (characters) + Links (strength of links) Nodes can be coloured (information on characters) Force-directed graph http://bl.ocks.org/mbostock/4062045
  78. 78. Before we start maps Networks Références A CLASSICAL NETWORK EXAMPLE Relationships of all of Victor Hugo’s characters of "Les Miserables". Data composed of nodes (characters) + Links (strength of links) Nodes can be coloured (information on characters) Force-directed graph Here, done with d3.js http://bl.ocks.org/mbostock/4062045
  79. 79. Before we start maps Networks Références A CLASSICAL NETWORK EXAMPLE Relationships of all of Victor Hugo’s characters of "Les Miserables".
  80. 80. Before we start maps Networks Références A CLASSICAL NETWORK EXAMPLE What do we see ? An overview of problems : Not the same graph each time (nondeterminstic algorithm)
  81. 81. Before we start maps Networks Références A CLASSICAL NETWORK EXAMPLE What do we see ? An overview of problems : Not the same graph each time (nondeterminstic algorithm) Spacial position does not encode attributes (algorithm-driven)
  82. 82. Before we start maps Networks Références A CLASSICAL NETWORK EXAMPLE What do we see ? An overview of problems : Not the same graph each time (nondeterminstic algorithm) Spacial position does not encode attributes (algorithm-driven) Proximity (of nodes) sometimes meaningful sometimes arbitrary. Artefacts created (some nodes “pushed back”.)
  83. 83. Before we start maps Networks Références A CLASSICAL NETWORK EXAMPLE What do we see ? An overview of problems : Not the same graph each time (nondeterminstic algorithm) Spacial position does not encode attributes (algorithm-driven) Proximity (of nodes) sometimes meaningful sometimes arbitrary. Artefacts created (some nodes “pushed back”.) What can we learn ? What can we compare ?
  84. 84. Before we start maps Networks Références A CLASSICAL NETWORK EXAMPLE What do we see ? An overview of problems : Not the same graph each time (nondeterminstic algorithm) Spacial position does not encode attributes (algorithm-driven) Proximity (of nodes) sometimes meaningful sometimes arbitrary. Artefacts created (some nodes “pushed back”.) What can we learn ? What can we compare ? Alternative view with bezier http://bl.ocks.org/mbostock/4600693
  85. 85. Before we start maps Networks Références HOW DOES IT WORK ? A vast family of Force-directed placement network representation Placing nodes randomly (put colour on groups)
  86. 86. Before we start maps Networks Références HOW DOES IT WORK ? A vast family of Force-directed placement network representation Placing nodes randomly (put colour on groups) Iteratively refine location following “forces”
  87. 87. Before we start maps Networks Références HOW DOES IT WORK ? A vast family of Force-directed placement network representation Placing nodes randomly (put colour on groups) Iteratively refine location following “forces” Nodes have “repulsive forces” (like electrons)
  88. 88. Before we start maps Networks Références HOW DOES IT WORK ? A vast family of Force-directed placement network representation Placing nodes randomly (put colour on groups) Iteratively refine location following “forces” Nodes have “repulsive forces” (like electrons) Links are “springs” (strength proportional to nb of connections)
  89. 89. Before we start maps Networks Références HOW DOES IT WORK ? A vast family of Force-directed placement network representation Placing nodes randomly (put colour on groups) Iteratively refine location following “forces” Nodes have “repulsive forces” (like electrons) Links are “springs” (strength proportional to nb of connections) Apply “some other rules‘” (see next slides)
  90. 90. Before we start maps Networks Références HOW DOES IT WORK ? A vast family of Force-directed placement network representation Placing nodes randomly (put colour on groups) Iteratively refine location following “forces” Nodes have “repulsive forces” (like electrons) Links are “springs” (strength proportional to nb of connections) Apply “some other rules‘” (see next slides) Iterate (computationally demanding)
  91. 91. Before we start maps Networks Références HOW DOES IT WORK ? A vast family of Force-directed placement network representation Placing nodes randomly (put colour on groups) Iteratively refine location following “forces” Nodes have “repulsive forces” (like electrons) Links are “springs” (strength proportional to nb of connections) Apply “some other rules‘” (see next slides) Iterate (computationally demanding) Wait until it stabilizes (even on a local equilibrium)
  92. 92. Before we start maps Networks Références FORCE-DIRECTED GRAPHS : Physically-based model + aesthetic touch + fuzzy stuff... From Tunkelang (1998)
  93. 93. Before we start maps Networks Références RULES FOR “NODE-LINK” REPRESENTATIONS Node-links representation follow rules : General rules : From Katya Ognyanova (2015) and Bahoken et al. (2013)
  94. 94. Before we start maps Networks Références RULES FOR “NODE-LINK” REPRESENTATIONS Node-links representation follow rules : General rules : Most connected nodes in the center From Katya Ognyanova (2015) and Bahoken et al. (2013)
  95. 95. Before we start maps Networks Références RULES FOR “NODE-LINK” REPRESENTATIONS Node-links representation follow rules : General rules : Most connected nodes in the center Less connected nodes at the periphery From Katya Ognyanova (2015) and Bahoken et al. (2013)
  96. 96. Before we start maps Networks Références RULES FOR “NODE-LINK” REPRESENTATIONS Node-links representation follow rules : General rules : Most connected nodes in the center Less connected nodes at the periphery Layout aesthetics rules : From Katya Ognyanova (2015) and Bahoken et al. (2013)
  97. 97. Before we start maps Networks Références RULES FOR “NODE-LINK” REPRESENTATIONS Node-links representation follow rules : General rules : Most connected nodes in the center Less connected nodes at the periphery Layout aesthetics rules : Minimize edge crossing From Katya Ognyanova (2015) and Bahoken et al. (2013)
  98. 98. Before we start maps Networks Références RULES FOR “NODE-LINK” REPRESENTATIONS Node-links representation follow rules : General rules : Most connected nodes in the center Less connected nodes at the periphery Layout aesthetics rules : Minimize edge crossing Prevent overlap From Katya Ognyanova (2015) and Bahoken et al. (2013)
  99. 99. Before we start maps Networks Références RULES FOR “NODE-LINK” REPRESENTATIONS Node-links representation follow rules : General rules : Most connected nodes in the center Less connected nodes at the periphery Layout aesthetics rules : Minimize edge crossing Prevent overlap Symmetry From Katya Ognyanova (2015) and Bahoken et al. (2013)
  100. 100. Before we start maps Networks Références RULES FOR “NODE-LINK” REPRESENTATIONS Node-links representation follow rules : General rules : Most connected nodes in the center Less connected nodes at the periphery Layout aesthetics rules : Minimize edge crossing Prevent overlap Symmetry Uniform edge length From Katya Ognyanova (2015) and Bahoken et al. (2013)
  101. 101. Before we start maps Networks Références LAYOUT AESTHETICS From Katya Ognyanova (2015)
  102. 102. Before we start maps Networks Références A NOTE ON DATA : MATRIX From Katya Ognyanova (2015)
  103. 103. Before we start maps Networks Références A NOTE ON DATA : EDGE LIST From Katya Ognyanova (2015)
  104. 104. Before we start maps Networks Références A NOTE ON DATA : NODE LIST From Katya Ognyanova (2015)
  105. 105. Before we start maps Networks Références A NOTE ON DATA : NODE ATTRIBUTES From Katya Ognyanova (2015)
  106. 106. Before we start maps Networks Références ALTERNATIVE VIEW : ADJACENT MATRIX PLOT An adjacency matrix, where each cell ij represents an edge from vertex i to vertex j. Here, vertices represent characters in a book, while edges represent co-occurrence in a chapter. http://bost.ocks.org/mike/miserables/
  107. 107. Before we start maps Networks Références NETWORKS : ADJACENT MATRIX PLOT As in many example, sorting is very useful !
  108. 108. Before we start maps Networks Références NETWORKS : ADJACENT MATRIX PLOT Adjacent matrix help identifying patterns From McGuffin (2012)
  109. 109. Before we start maps Networks Références NETWORKS : ARC DIAGRAM PLOT To put some structure in the graph : Horizontal alignment From Gaston Sanchez see also McGuffin (2012)
  110. 110. Before we start maps Networks Références NETWORKS : ARC DIAGRAM PLOT Now we can sort ! (according to groups, and then degrees) FromGaston Sanchez
  111. 111. Before we start maps Networks Références NETWORKS : ARC DIAGRAM PLOT : MY TOUCH !
  112. 112. Before we start maps Networks Références NETWORKS : ROLE OF TOPOLOGY Circular layouts of a 43-node, 80-edge network. Force-directed graph From McGuffin (2012)
  113. 113. Before we start maps Networks Références NETWORKS : ADDING A TOPOLOGY HELPS Circular layouts of a 43-node, 80-edge network. No ordering, with curved edges. From McGuffin (2012)
  114. 114. Before we start maps Networks Références NETWORKS : ADDING A TOPOLOGY HELPS Circular layouts of a 43-node, 80-edge network. Barycenter ordering, with curved edges.
  115. 115. Before we start maps Networks Références PROBLEM 1 : IDENTIFY REFERENCE GRAPHS Random Graphs difficult to identify
  116. 116. Before we start maps Networks Références PROBLEM 1 : IDENTIFY REFERENCE GRAPHS Random Graphs difficult to identify
  117. 117. Before we start maps Networks Références PROBLEM 1 : IDENTIFY REFERENCE GRAPHS Random Graphs difficult to identify Or
  118. 118. Before we start maps Networks Références PROBLEM 1 : IDENTIFY REFERENCE GRAPHS Random Graphs difficult to identify Or
  119. 119. Before we start maps Networks Références PROBLEM 1 : IDENTIFY REFERENCE GRAPHS Random Graphs difficult to identify Or → How to do any visual test ?
  120. 120. Before we start maps Networks Références PROBLEM 2 : LAYOUT CHANGE PERCEPTION Three layouts of the same graph From Kolaczyk and Csárdi (2014)
  121. 121. Before we start maps Networks Références PROBLEM 2 : LAYOUT CHANGE PERCEPTION Three layouts of the same graph From Kolaczyk and Csárdi (2014)
  122. 122. Before we start maps Networks Références PROBLEM 2 : LAYOUT CHANGE PERCEPTION Three layouts of the same graph From Kolaczyk and Csárdi (2014)
  123. 123. Before we start maps Networks Références PROBLEM 2 : LAYOUT CHANGE PERCEPTION Three layouts of the same graph → Circular, radial, layered. From Kolaczyk and Csárdi (2014)
  124. 124. Before we start maps Networks Références PROBLEM 3 : DECORATION HELPS ? From Kolaczyk and Csárdi (2014)
  125. 125. Before we start maps Networks Références PROBLEM 3 : DECORATION HELPS ? Plain and decorated visualizations of a karate club network From Kolaczyk and Csárdi (2014)
  126. 126. Before we start maps Networks Références PROBLEM 4 : SCALABILITY ? (192 blogs linked by 1,431 edges indicating at least one of the two blogs referenced the other). From Kolaczyk and Csárdi (2014)
  127. 127. Before we start maps Networks Références PROBLEM 4 : SCALABILITY ? French political blog network (192 blogs linked by 1,431 edges indicating at least one of the two blogs referenced the other). From Kolaczyk and Csárdi (2014)
  128. 128. Before we start maps Networks Références PROBLEM 4 : SCALABILITY ? French political blog network Towards an "hairball" + complexity is O(N2 v) (192 blogs linked by 1,431 edges indicating at least one of the two blogs referenced the other). From Kolaczyk and Csárdi (2014)
  129. 129. Before we start maps Networks Références SOLUTIONS : SPLIT ? From Kolaczyk and Csárdi (2014)
  130. 130. Before we start maps Networks Références SOLUTIONS : SPLIT ? Two ego-centric views of the karate club network From Kolaczyk and Csárdi (2014)
  131. 131. Before we start maps Networks Références SOLUTIONS : AGGREGATE ? From Kolaczyk and Csárdi (2014)
  132. 132. Before we start maps Networks Références SOLUTIONS : AGGREGATE ? French political blog network at the level of political parties From Kolaczyk and Csárdi (2014)
  133. 133. Before we start maps Networks Références REFERENCES I Bahoken, F., Beauguitte, L., and Lhomme, S. (2013). La visualisation des réseaux. principes, enjeux et perspectives. Bertin, J. (1981). Théorie matricielle de la graphique. Communication et langages, 48(1) :62–74. Buja, A., Cook, D., Hofmann, H., Lawrence, M., Lee, E.-K., Swayne, D. F., and Wickham, H. (2009). Statistical inference for exploratory data analysis and model diagnostics. Philosophical Transactions of the Royal Society of London A : Mathematical, Physical and Engineering Sciences, 367(1906) :4361–4383. Gelman, A. (2004). Exploratory data analysis for complex models. Journal of Computational and Graphical Statistics, 13(4). Heijmans, R., Heuver, R., Levallois, C., and Lelyveld, I. V. (2014). Dynamic visualization of large transaction networks : the daily dutch overnight money market. Kolaczyk, E. D. (2009). Statistical Analysis of Network Data : Methods and Models. Springer Series in Statistics. Springer-Verlag New York, 1 edition. Kolaczyk, E. D. and Csárdi, G. (2014). Statistical Analysis of Network Data with R. Use R ! 65. Springer-Verlag New York, 1 edition.
  134. 134. Before we start maps Networks Références REFERENCES II McGuffin, M. J. (2012). Simple algorithms for network visualization : A tutorial. Tsinghua Science and Technology, 17(4) :383–398. Ognyanova, K. (2015). R network visualization workshop. In POLNET 2015, Portland OR. Tufte, E. R. (2001). The Visual Display of Quantitative Information. Graphics Press, 2 edition. Tunkelang, D. (1998). A Numerical Optimization Approach to Graph Drawing. PhD thesis, Dissertation, Carnegie Mellon University, School of Computer Science.

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