SlideShare a Scribd company logo

Why and How to Visualize Data with Maps & Networks

•
•
C
Christophe Bontemps

Slides presented at the 2017, January 31st Toulouse Data Visualization Meetup on maps & Neworks. Please use, share and cite.

Data & Analytics
1 of 134
Before we start maps Networks Références Maps& Networks : Why and How to Visualize ? Christophe Bontemps Toulouse School of Economics, INRA @Xtophe_Bontemps
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 : Data exploration Graphs as visual tests, comparisons (short time to built and to read)
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)
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”
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
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)
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
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 (too).” Gelman (2004)
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)
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)
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)
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)
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
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 ?
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 !
Before we start maps Networks Références A CASE STUDY : ELECTIONS Most of the election’s reports are maps : legitimate questions What happened ?
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 ?
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 ?
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 ?
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 legitimate questions ! Quantitative information is over represented in "large" states
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
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
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
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)
Before we start maps Networks Références A CASE STUDY : ELECTIONS Maybe a better map ? From Financial Times blog From Financial Times
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 Andrew Gelman’s Blog. Each pixel represents 1,000 votes.
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, lines,
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 area, colorpleth,etc..)
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 :
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 ?
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 ?
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)
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)
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
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
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)
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 !)
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) between geographical entities.
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)
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 create adjacency
Before we start maps Networks Références TYPES OF NETWORKS Adjacency Networks Bipartite Networks From bipartite, one can create adjacency
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)
Before we start maps Networks Références TYPES OF NETWORKS Undirected (A& B ’know each other’) and directed (A ’owe money to’ B)
Before we start maps Networks Références TYPES OF NETWORKS Undirected (A& B ’know each other’) and directed (A ’owe money to’ B)
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)
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) ; and star (bottom right)
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)
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
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
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
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
Before we start maps Networks Références A CLASSICAL NETWORK EXAMPLE Relationships of all of Victor Hugo’s characters of "Les Miserables".
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)
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)
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”.)
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 ?
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
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)
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”
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)
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)
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)
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)
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)
Before we start maps Networks Références FORCE-DIRECTED GRAPHS : Physically-based model + aesthetic touch + fuzzy stuff... From Tunkelang (1998)
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)
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)
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)
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)
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)
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)
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)
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)
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 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/
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 McGuffin (2012)
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)
Before we start maps Networks Références NETWORKS : ARC DIAGRAM PLOT Now we can sort ! (according to groups, and then degrees) FromGaston Sanchez
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-directed graph From McGuffin (2012)
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)
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.
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 to do any visual test ?
Before we start maps Networks Références PROBLEM 2 : LAYOUT CHANGE PERCEPTION Three layouts of the same graph From Kolaczyk and Csárdi (2014)
Before we start maps Networks Références PROBLEM 2 : LAYOUT CHANGE PERCEPTION Three layouts of the same graph From Kolaczyk and Csárdi (2014)
Before we start maps Networks Références PROBLEM 2 : LAYOUT CHANGE PERCEPTION Three layouts of the same graph From Kolaczyk and Csárdi (2014)
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)
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 club network From Kolaczyk and Csárdi (2014)
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)
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)
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)
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 Kolaczyk and Csárdi (2014)
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 parties From Kolaczyk and Csárdi (2014)
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.
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.

Recommended

Meetup D3.js Paris - Christophe Blefari - Equancy
Meetup D3.js Paris - Christophe Blefari - EquancyMeetup D3.js Paris - Christophe Blefari - Equancy
Meetup D3.js Paris - Christophe Blefari - EquancyChristophe Blefari
 
Intro to Internet Mapping (epan 2011)
Intro to Internet Mapping (epan 2011)Intro to Internet Mapping (epan 2011)
Intro to Internet Mapping (epan 2011)WV Assocation of Geospatial Professionals
 
Crime mapping conference 2009
Crime mapping conference 2009Crime mapping conference 2009
Crime mapping conference 2009Talke Hoppmann
 
Data science week_2_visualization
Data science week_2_visualizationData science week_2_visualization
Data science week_2_visualizationKeiko Ono
 
Bringing Cities to Life Using Big Data & WebGL
Bringing Cities to Life Using Big Data & WebGLBringing Cities to Life Using Big Data & WebGL
Bringing Cities to Life Using Big Data & WebGLRobin Hawkes
 
Pie chart or pizza: identifying chart types and their virality on Twitter
Pie chart or pizza: identifying chart types and their virality on TwitterPie chart or pizza: identifying chart types and their virality on Twitter
Pie chart or pizza: identifying chart types and their virality on TwitterElena Simperl
 
Analysing charts and graphics
Analysing charts and graphicsAnalysing charts and graphics
Analysing charts and graphicsŠkola Futura
 
Data as a Creative Material
Data as a Creative MaterialData as a Creative Material
Data as a Creative MaterialAudree Lapierre
 

Recommended

Meetup D3.js Paris - Christophe Blefari - Equancy
Meetup D3.js Paris - Christophe Blefari - EquancyMeetup D3.js Paris - Christophe Blefari - Equancy
Meetup D3.js Paris - Christophe Blefari - EquancyChristophe Blefari
 
Intro to Internet Mapping (epan 2011)
Intro to Internet Mapping (epan 2011)Intro to Internet Mapping (epan 2011)
Intro to Internet Mapping (epan 2011)WV Assocation of Geospatial Professionals
 
Crime mapping conference 2009
Crime mapping conference 2009Crime mapping conference 2009
Crime mapping conference 2009Talke Hoppmann
 
Data science week_2_visualization
Data science week_2_visualizationData science week_2_visualization
Data science week_2_visualizationKeiko Ono
 
Bringing Cities to Life Using Big Data & WebGL
Bringing Cities to Life Using Big Data & WebGLBringing Cities to Life Using Big Data & WebGL
Bringing Cities to Life Using Big Data & WebGLRobin Hawkes
 
Pie chart or pizza: identifying chart types and their virality on Twitter
Pie chart or pizza: identifying chart types and their virality on TwitterPie chart or pizza: identifying chart types and their virality on Twitter
Pie chart or pizza: identifying chart types and their virality on TwitterElena Simperl
 
Analysing charts and graphics
Analysing charts and graphicsAnalysing charts and graphics
Analysing charts and graphicsŠkola Futura
 
Data as a Creative Material
Data as a Creative MaterialData as a Creative Material
Data as a Creative MaterialAudree Lapierre
 
ASOCEU - Lesson 2 - Data Refining (slides)
ASOCEU - Lesson 2 - Data Refining (slides)ASOCEU - Lesson 2 - Data Refining (slides)
ASOCEU - Lesson 2 - Data Refining (slides)A Scuola di OpenCoesione
 
Data visualisations quality aspects
Data visualisations quality aspectsData visualisations quality aspects
Data visualisations quality aspectsAntonio De Marinis
 
What is my neighbourhood like
What is my neighbourhood likeWhat is my neighbourhood like
What is my neighbourhood likemycommunitylocality
 
What is my neighbourhood like: Data collecting
What is my neighbourhood like: Data collectingWhat is my neighbourhood like: Data collecting
What is my neighbourhood like: Data collectingAmarni Wood
 
NCompass Live: Presenting Data in Meaningful and Interesting Ways
NCompass Live: Presenting Data in Meaningful and Interesting WaysNCompass Live: Presenting Data in Meaningful and Interesting Ways
NCompass Live: Presenting Data in Meaningful and Interesting WaysNebraska Library Commission
 
OpenStreetMap - The Quality Issue
OpenStreetMap - The Quality IssueOpenStreetMap - The Quality Issue
OpenStreetMap - The Quality IssueCASA, UCL
 
Data Visualization.pptx
Data Visualization.pptxData Visualization.pptx
Data Visualization.pptxSriniRao31
 
Data that sings Only in Seattle Presentation Oct 9 2014
Data that sings Only in Seattle Presentation Oct 9 2014Data that sings Only in Seattle Presentation Oct 9 2014
Data that sings Only in Seattle Presentation Oct 9 2014onlyinseattle
 
A Journey to the World of GIS
A Journey to the World of GISA Journey to the World of GIS
A Journey to the World of GISNishant Sinha
 
GEOG100--Lecture 03--Maps
GEOG100--Lecture 03--MapsGEOG100--Lecture 03--Maps
GEOG100--Lecture 03--Mapsangelaorr
 
Beyond bar charts best practices in data visualization
Beyond bar charts best practices in data visualizationBeyond bar charts best practices in data visualization
Beyond bar charts best practices in data visualizationBen Jones
 
Applying data visualisation to the analytics process
Applying data visualisation to the analytics processApplying data visualisation to the analytics process
Applying data visualisation to the analytics processCasper Blicher Olsen
 
Topological Data Analysis of Complex Spatial Systems
Topological Data Analysis of Complex Spatial SystemsTopological Data Analysis of Complex Spatial Systems
Topological Data Analysis of Complex Spatial SystemsMason Porter
 
Coding community: Geographic information technologies and mappings of the cit...
Coding community: Geographic information technologies and mappings of the cit...Coding community: Geographic information technologies and mappings of the cit...
Coding community: Geographic information technologies and mappings of the cit...Matthew Wilson
 
WDE08 Visualizing Web of Data
WDE08 Visualizing Web of DataWDE08 Visualizing Web of Data
WDE08 Visualizing Web of DataSatoshi Kikuchi
 
Geographic Information Systems PowerPoint Summer 2014 Semester
Geographic Information Systems PowerPoint Summer 2014 SemesterGeographic Information Systems PowerPoint Summer 2014 Semester
Geographic Information Systems PowerPoint Summer 2014 SemesterNicholas Tancredi
 
Data/Visualization - Digital Center Cohort - 13_0222
Data/Visualization - Digital Center Cohort - 13_0222Data/Visualization - Digital Center Cohort - 13_0222
Data/Visualization - Digital Center Cohort - 13_0222jeffreylancaster
 
A step towards interactive displays of digital elevation models
A step towards interactive displays of digital elevation modelsA step towards interactive displays of digital elevation models
A step towards interactive displays of digital elevation modelsExtensión del Instituto Tecnológico de Tuxtla Gutiérrez sede Bochil
 
CSUN 2023 Automated Descriptions 3 March 2023 TG.pptx
CSUN 2023 Automated Descriptions 3 March 2023 TG.pptxCSUN 2023 Automated Descriptions 3 March 2023 TG.pptx
CSUN 2023 Automated Descriptions 3 March 2023 TG.pptxTed Gies
 
GIS, Geospatial Data and You
GIS, Geospatial Data and YouGIS, Geospatial Data and You
GIS, Geospatial Data and YouMike Mahaffie
 
Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdf
Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdfMarket Analysis in the 5 Largest Economic Countries in Southeast Asia.pdf
Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdfRachmat Ramadhan H
 
Schema on read is obsolete. Welcome metaprogramming..pdf
Schema on read is obsolete. Welcome metaprogramming..pdfSchema on read is obsolete. Welcome metaprogramming..pdf
Schema on read is obsolete. Welcome metaprogramming..pdfLars Albertsson
 

More Related Content

Similar to Why and How to Visualize Data with Maps & Networks

ASOCEU - Lesson 2 - Data Refining (slides)
ASOCEU - Lesson 2 - Data Refining (slides)ASOCEU - Lesson 2 - Data Refining (slides)
ASOCEU - Lesson 2 - Data Refining (slides)A Scuola di OpenCoesione
 
Data visualisations quality aspects
Data visualisations quality aspectsData visualisations quality aspects
Data visualisations quality aspectsAntonio De Marinis
 
What is my neighbourhood like: Data collecting
What is my neighbourhood like: Data collectingWhat is my neighbourhood like: Data collecting
What is my neighbourhood like: Data collectingAmarni Wood
 
NCompass Live: Presenting Data in Meaningful and Interesting Ways
NCompass Live: Presenting Data in Meaningful and Interesting WaysNCompass Live: Presenting Data in Meaningful and Interesting Ways
NCompass Live: Presenting Data in Meaningful and Interesting WaysNebraska Library Commission
 
OpenStreetMap - The Quality Issue
OpenStreetMap - The Quality IssueOpenStreetMap - The Quality Issue
OpenStreetMap - The Quality IssueCASA, UCL
 
Data Visualization.pptx
Data Visualization.pptxData Visualization.pptx
Data Visualization.pptxSriniRao31
 
Data that sings Only in Seattle Presentation Oct 9 2014
Data that sings Only in Seattle Presentation Oct 9 2014Data that sings Only in Seattle Presentation Oct 9 2014
Data that sings Only in Seattle Presentation Oct 9 2014onlyinseattle
 
A Journey to the World of GIS
A Journey to the World of GISA Journey to the World of GIS
A Journey to the World of GISNishant Sinha
 
GEOG100--Lecture 03--Maps
GEOG100--Lecture 03--MapsGEOG100--Lecture 03--Maps
GEOG100--Lecture 03--Mapsangelaorr
 
Beyond bar charts best practices in data visualization
Beyond bar charts best practices in data visualizationBeyond bar charts best practices in data visualization
Beyond bar charts best practices in data visualizationBen Jones
 
Applying data visualisation to the analytics process
Applying data visualisation to the analytics processApplying data visualisation to the analytics process
Applying data visualisation to the analytics processCasper Blicher Olsen
 
Topological Data Analysis of Complex Spatial Systems
Topological Data Analysis of Complex Spatial SystemsTopological Data Analysis of Complex Spatial Systems
Topological Data Analysis of Complex Spatial SystemsMason Porter
 
Coding community: Geographic information technologies and mappings of the cit...
Coding community: Geographic information technologies and mappings of the cit...Coding community: Geographic information technologies and mappings of the cit...
Coding community: Geographic information technologies and mappings of the cit...Matthew Wilson
 
WDE08 Visualizing Web of Data
WDE08 Visualizing Web of DataWDE08 Visualizing Web of Data
WDE08 Visualizing Web of DataSatoshi Kikuchi
 
Geographic Information Systems PowerPoint Summer 2014 Semester
Geographic Information Systems PowerPoint Summer 2014 SemesterGeographic Information Systems PowerPoint Summer 2014 Semester
Geographic Information Systems PowerPoint Summer 2014 SemesterNicholas Tancredi
 
Data/Visualization - Digital Center Cohort - 13_0222
Data/Visualization - Digital Center Cohort - 13_0222Data/Visualization - Digital Center Cohort - 13_0222
Data/Visualization - Digital Center Cohort - 13_0222jeffreylancaster
 
CSUN 2023 Automated Descriptions 3 March 2023 TG.pptx
CSUN 2023 Automated Descriptions 3 March 2023 TG.pptxCSUN 2023 Automated Descriptions 3 March 2023 TG.pptx
CSUN 2023 Automated Descriptions 3 March 2023 TG.pptxTed Gies
 
GIS, Geospatial Data and You
GIS, Geospatial Data and YouGIS, Geospatial Data and You
GIS, Geospatial Data and YouMike Mahaffie
 

Similar to Why and How to Visualize Data with Maps & Networks (20)

ASOCEU - Lesson 2 - Data Refining (slides)
ASOCEU - Lesson 2 - Data Refining (slides)ASOCEU - Lesson 2 - Data Refining (slides)
ASOCEU - Lesson 2 - Data Refining (slides)
 
Data visualisations quality aspects
Data visualisations quality aspectsData visualisations quality aspects
Data visualisations quality aspects
 
What is my neighbourhood like
What is my neighbourhood likeWhat is my neighbourhood like
What is my neighbourhood like
 
What is my neighbourhood like: Data collecting
What is my neighbourhood like: Data collectingWhat is my neighbourhood like: Data collecting
What is my neighbourhood like: Data collecting
 
NCompass Live: Presenting Data in Meaningful and Interesting Ways
NCompass Live: Presenting Data in Meaningful and Interesting WaysNCompass Live: Presenting Data in Meaningful and Interesting Ways
NCompass Live: Presenting Data in Meaningful and Interesting Ways
 
OpenStreetMap - The Quality Issue
OpenStreetMap - The Quality IssueOpenStreetMap - The Quality Issue
OpenStreetMap - The Quality Issue
 
Data Visualization.pptx
Data Visualization.pptxData Visualization.pptx
Data Visualization.pptx
 
Data that sings Only in Seattle Presentation Oct 9 2014
Data that sings Only in Seattle Presentation Oct 9 2014Data that sings Only in Seattle Presentation Oct 9 2014
Data that sings Only in Seattle Presentation Oct 9 2014
 
A Journey to the World of GIS
A Journey to the World of GISA Journey to the World of GIS
A Journey to the World of GIS
 
GEOG100--Lecture 03--Maps
GEOG100--Lecture 03--MapsGEOG100--Lecture 03--Maps
GEOG100--Lecture 03--Maps
 
Beyond bar charts best practices in data visualization
Beyond bar charts best practices in data visualizationBeyond bar charts best practices in data visualization
Beyond bar charts best practices in data visualization
 
Applying data visualisation to the analytics process
Applying data visualisation to the analytics processApplying data visualisation to the analytics process
Applying data visualisation to the analytics process
 
Topological Data Analysis of Complex Spatial Systems
Topological Data Analysis of Complex Spatial SystemsTopological Data Analysis of Complex Spatial Systems
Topological Data Analysis of Complex Spatial Systems
 
Coding community: Geographic information technologies and mappings of the cit...
Coding community: Geographic information technologies and mappings of the cit...Coding community: Geographic information technologies and mappings of the cit...
Coding community: Geographic information technologies and mappings of the cit...
 
WDE08 Visualizing Web of Data
WDE08 Visualizing Web of DataWDE08 Visualizing Web of Data
WDE08 Visualizing Web of Data
 
Geographic Information Systems PowerPoint Summer 2014 Semester
Geographic Information Systems PowerPoint Summer 2014 SemesterGeographic Information Systems PowerPoint Summer 2014 Semester
Geographic Information Systems PowerPoint Summer 2014 Semester
 
Data/Visualization - Digital Center Cohort - 13_0222
Data/Visualization - Digital Center Cohort - 13_0222Data/Visualization - Digital Center Cohort - 13_0222
Data/Visualization - Digital Center Cohort - 13_0222
 
A step towards interactive displays of digital elevation models
A step towards interactive displays of digital elevation modelsA step towards interactive displays of digital elevation models
A step towards interactive displays of digital elevation models
 
CSUN 2023 Automated Descriptions 3 March 2023 TG.pptx
CSUN 2023 Automated Descriptions 3 March 2023 TG.pptxCSUN 2023 Automated Descriptions 3 March 2023 TG.pptx
CSUN 2023 Automated Descriptions 3 March 2023 TG.pptx
 
GIS, Geospatial Data and You
GIS, Geospatial Data and YouGIS, Geospatial Data and You
GIS, Geospatial Data and You
 

Recently uploaded

Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdf
Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdfMarket Analysis in the 5 Largest Economic Countries in Southeast Asia.pdf
Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdfRachmat Ramadhan H
 
Schema on read is obsolete. Welcome metaprogramming..pdf
Schema on read is obsolete. Welcome metaprogramming..pdfSchema on read is obsolete. Welcome metaprogramming..pdf
Schema on read is obsolete. Welcome metaprogramming..pdfLars Albertsson
 
VIP Call Girls in Amravati Aarohi 8250192130 Independent Escort Service Amravati
VIP Call Girls in Amravati Aarohi 8250192130 Independent Escort Service AmravatiVIP Call Girls in Amravati Aarohi 8250192130 Independent Escort Service Amravati
VIP Call Girls in Amravati Aarohi 8250192130 Independent Escort Service AmravatiSuhani Kapoor
 
Unveiling Insights: The Role of a Data Analyst
Unveiling Insights: The Role of a Data AnalystUnveiling Insights: The Role of a Data Analyst
Unveiling Insights: The Role of a Data AnalystSamantha Rae Coolbeth
 
dokumen.tips_chapter-4-transient-heat-conduction-mehmet-kanoglu.ppt
dokumen.tips_chapter-4-transient-heat-conduction-mehmet-kanoglu.pptdokumen.tips_chapter-4-transient-heat-conduction-mehmet-kanoglu.ppt
dokumen.tips_chapter-4-transient-heat-conduction-mehmet-kanoglu.pptSonatrach
 
100-Concepts-of-AI by Anupama Kate .pptx
100-Concepts-of-AI by Anupama Kate .pptx100-Concepts-of-AI by Anupama Kate .pptx
100-Concepts-of-AI by Anupama Kate .pptxAnupama Kate
 
VIP High Class Call Girls Jamshedpur Anushka 8250192130 Independent Escort Se...
VIP High Class Call Girls Jamshedpur Anushka 8250192130 Independent Escort Se...VIP High Class Call Girls Jamshedpur Anushka 8250192130 Independent Escort Se...
VIP High Class Call Girls Jamshedpur Anushka 8250192130 Independent Escort Se...Suhani Kapoor
 
BigBuy dropshipping via API with DroFx.pptx
BigBuy dropshipping via API with DroFx.pptxBigBuy dropshipping via API with DroFx.pptx
BigBuy dropshipping via API with DroFx.pptxolyaivanovalion
 
BabyOno dropshipping via API with DroFx.pptx
BabyOno dropshipping via API with DroFx.pptxBabyOno dropshipping via API with DroFx.pptx
BabyOno dropshipping via API with DroFx.pptxolyaivanovalion
 
(ISHITA) Call Girls Service Hyderabad Call Now 8617697112 Hyderabad Escorts
(ISHITA) Call Girls Service Hyderabad Call Now 8617697112 Hyderabad Escorts(ISHITA) Call Girls Service Hyderabad Call Now 8617697112 Hyderabad Escorts
(ISHITA) Call Girls Service Hyderabad Call Now 8617697112 Hyderabad EscortsCall girls in Ahmedabad High profile
 
CebaBaby dropshipping via API with DroFX.pptx
CebaBaby dropshipping via API with DroFX.pptxCebaBaby dropshipping via API with DroFX.pptx
CebaBaby dropshipping via API with DroFX.pptxolyaivanovalion
 
Dubai Call Girls Wifey O52&786472 Call Girls Dubai
Dubai Call Girls Wifey O52&786472 Call Girls DubaiDubai Call Girls Wifey O52&786472 Call Girls Dubai
Dubai Call Girls Wifey O52&786472 Call Girls Dubaihf8803863
 
Smarteg dropshipping via API with DroFx.pptx
Smarteg dropshipping via API with DroFx.pptxSmarteg dropshipping via API with DroFx.pptx
Smarteg dropshipping via API with DroFx.pptxolyaivanovalion
 
Generative AI on Enterprise Cloud with NiFi and Milvus
Generative AI on Enterprise Cloud with NiFi and MilvusGenerative AI on Enterprise Cloud with NiFi and Milvus
Generative AI on Enterprise Cloud with NiFi and MilvusTimothy Spann
 
Al Barsha Escorts $#$ O565212860 $#$ Escort Service In Al Barsha
Al Barsha Escorts $#$ O565212860 $#$ Escort Service In Al BarshaAl Barsha Escorts $#$ O565212860 $#$ Escort Service In Al Barsha
Al Barsha Escorts $#$ O565212860 $#$ Escort Service In Al BarshaAroojKhan71
 
Midocean dropshipping via API with DroFx
Midocean dropshipping via API with DroFxMidocean dropshipping via API with DroFx
Midocean dropshipping via API with DroFxolyaivanovalion
 
Brighton SEO | April 2024 | Data Storytelling
Brighton SEO | April 2024 | Data StorytellingBrighton SEO | April 2024 | Data Storytelling
Brighton SEO | April 2024 | Data StorytellingNeil Barnes
 
Carero dropshipping via API with DroFx.pptx
Carero dropshipping via API with DroFx.pptxCarero dropshipping via API with DroFx.pptx
Carero dropshipping via API with DroFx.pptxolyaivanovalion
 
Industrialised data - the key to AI success.pdf
Industrialised data - the key to AI success.pdfIndustrialised data - the key to AI success.pdf
Industrialised data - the key to AI success.pdfLars Albertsson
 

Recently uploaded (20)

Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdf
Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdfMarket Analysis in the 5 Largest Economic Countries in Southeast Asia.pdf
Market Analysis in the 5 Largest Economic Countries in Southeast Asia.pdf
 
Schema on read is obsolete. Welcome metaprogramming..pdf
Schema on read is obsolete. Welcome metaprogramming..pdfSchema on read is obsolete. Welcome metaprogramming..pdf
Schema on read is obsolete. Welcome metaprogramming..pdf
 
VIP Call Girls in Amravati Aarohi 8250192130 Independent Escort Service Amravati
VIP Call Girls in Amravati Aarohi 8250192130 Independent Escort Service AmravatiVIP Call Girls in Amravati Aarohi 8250192130 Independent Escort Service Amravati
VIP Call Girls in Amravati Aarohi 8250192130 Independent Escort Service Amravati
 
Unveiling Insights: The Role of a Data Analyst
Unveiling Insights: The Role of a Data AnalystUnveiling Insights: The Role of a Data Analyst
Unveiling Insights: The Role of a Data Analyst
 
VIP Call Girls Service Charbagh { Lucknow Call Girls Service 9548273370 } Boo...
VIP Call Girls Service Charbagh { Lucknow Call Girls Service 9548273370 } Boo...VIP Call Girls Service Charbagh { Lucknow Call Girls Service 9548273370 } Boo...
VIP Call Girls Service Charbagh { Lucknow Call Girls Service 9548273370 } Boo...
 
dokumen.tips_chapter-4-transient-heat-conduction-mehmet-kanoglu.ppt
dokumen.tips_chapter-4-transient-heat-conduction-mehmet-kanoglu.pptdokumen.tips_chapter-4-transient-heat-conduction-mehmet-kanoglu.ppt
dokumen.tips_chapter-4-transient-heat-conduction-mehmet-kanoglu.ppt
 
100-Concepts-of-AI by Anupama Kate .pptx
100-Concepts-of-AI by Anupama Kate .pptx100-Concepts-of-AI by Anupama Kate .pptx
100-Concepts-of-AI by Anupama Kate .pptx
 
VIP High Class Call Girls Jamshedpur Anushka 8250192130 Independent Escort Se...
VIP High Class Call Girls Jamshedpur Anushka 8250192130 Independent Escort Se...VIP High Class Call Girls Jamshedpur Anushka 8250192130 Independent Escort Se...
VIP High Class Call Girls Jamshedpur Anushka 8250192130 Independent Escort Se...
 
BigBuy dropshipping via API with DroFx.pptx
BigBuy dropshipping via API with DroFx.pptxBigBuy dropshipping via API with DroFx.pptx
BigBuy dropshipping via API with DroFx.pptx
 
BabyOno dropshipping via API with DroFx.pptx
BabyOno dropshipping via API with DroFx.pptxBabyOno dropshipping via API with DroFx.pptx
BabyOno dropshipping via API with DroFx.pptx
 
(ISHITA) Call Girls Service Hyderabad Call Now 8617697112 Hyderabad Escorts
(ISHITA) Call Girls Service Hyderabad Call Now 8617697112 Hyderabad Escorts(ISHITA) Call Girls Service Hyderabad Call Now 8617697112 Hyderabad Escorts
(ISHITA) Call Girls Service Hyderabad Call Now 8617697112 Hyderabad Escorts
 
CebaBaby dropshipping via API with DroFX.pptx
CebaBaby dropshipping via API with DroFX.pptxCebaBaby dropshipping via API with DroFX.pptx
CebaBaby dropshipping via API with DroFX.pptx
 
Dubai Call Girls Wifey O52&786472 Call Girls Dubai
Dubai Call Girls Wifey O52&786472 Call Girls DubaiDubai Call Girls Wifey O52&786472 Call Girls Dubai
Dubai Call Girls Wifey O52&786472 Call Girls Dubai
 
Smarteg dropshipping via API with DroFx.pptx
Smarteg dropshipping via API with DroFx.pptxSmarteg dropshipping via API with DroFx.pptx
Smarteg dropshipping via API with DroFx.pptx
 
Generative AI on Enterprise Cloud with NiFi and Milvus
Generative AI on Enterprise Cloud with NiFi and MilvusGenerative AI on Enterprise Cloud with NiFi and Milvus
Generative AI on Enterprise Cloud with NiFi and Milvus
 
Al Barsha Escorts $#$ O565212860 $#$ Escort Service In Al Barsha
Al Barsha Escorts $#$ O565212860 $#$ Escort Service In Al BarshaAl Barsha Escorts $#$ O565212860 $#$ Escort Service In Al Barsha
Al Barsha Escorts $#$ O565212860 $#$ Escort Service In Al Barsha
 
Midocean dropshipping via API with DroFx
Midocean dropshipping via API with DroFxMidocean dropshipping via API with DroFx
Midocean dropshipping via API with DroFx
 
Brighton SEO | April 2024 | Data Storytelling
Brighton SEO | April 2024 | Data StorytellingBrighton SEO | April 2024 | Data Storytelling
Brighton SEO | April 2024 | Data Storytelling
 
Carero dropshipping via API with DroFx.pptx
Carero dropshipping via API with DroFx.pptxCarero dropshipping via API with DroFx.pptx
Carero dropshipping via API with DroFx.pptx
 
Industrialised data - the key to AI success.pdf
Industrialised data - the key to AI success.pdfIndustrialised data - the key to AI success.pdf
Industrialised data - the key to AI success.pdf
 

Why and How to Visualize Data with Maps & Networks

  • 1. Before we start maps Networks Références Maps& Networks : Why and How to Visualize ? Christophe Bontemps Toulouse School of Economics, INRA @Xtophe_Bontemps
  • 2. Before we start maps Networks Références MY JOB
  • 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. 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. 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. 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. 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. 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. Before we start maps Networks Références THE BIG PICTURE
  • 10. Before we start maps Networks Références THE BIG PICTURE
  • 11. Before we start maps Networks Références THE BIG PICTURE
  • 12. Before we start maps Networks Références THE BIG PICTURE
  • 13. Before we start maps Networks Références THE BIG PICTURE
  • 14. Before we start maps Networks Références THE BIG PICTURE
  • 15. Before we start maps Networks Références THE BIG PICTURE
  • 16. Before we start maps Networks Références TO CITE A FEW “All graphs are comparisons” ...
  • 17. Before we start maps Networks Références TO CITE A FEW “All graphs are comparisons” ... “All of statistics are comparisons (too).” Gelman (2004)
  • 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. 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. 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. 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. 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. 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. 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. Before we start maps Networks Références A CASE STUDY : ELECTIONS Most of the election’s reports are maps : legitimate questions What happened ?
  • 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. 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. 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. Before we start maps Networks Références A CASE STUDY : ELECTIONS 2016 results as a map : Washington Post
  • 30. Before we start maps Networks Références A CASE STUDY : ELECTIONS 2012 results as a map : New York Time
  • 31. Before we start maps Networks Références A CASE STUDY : ELECTIONS 2008 results as a map : New York Time
  • 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. 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. 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. 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. 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. Before we start maps Networks Références A CASE STUDY : ELECTIONS Maybe a better map ? From Financial Times blog From Financial Times
  • 38. Before we start maps Networks Références A CASE STUDY : ELECTIONS 2012 results as a map : New York Time
  • 39. Before we start maps Networks Références A CASE STUDY : ELECTIONS Maybe spatial information is not the most relevant !
  • 40. Before we start maps Networks Références A CASE STUDY : ELECTIONS Maybe spatial information is not the most relevant !
  • 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. Before we start maps Networks Références GEOGRAPHY EXISTS IN THE DATA, BUT IS NOT THE MATTER OF COMPARISON
  • 43. Before we start maps Networks Références ANOTHER GOOD EXAMPLE FOR NOT USING A MAP From Our World in Data
  • 44. Before we start maps Networks Références EXAMPLES OF MAPS MISSISSIPPI MEANDERS The interest here is to compare location, lines,
  • 45. Before we start maps Networks Références AIR MAP OF THE WORLD (1943)
  • 46. Before we start maps Networks Références THE LONDON COUNTY COUNCIL BOMB DAMAGE MAPS, 1939-1945
  • 47. Before we start maps Networks Références AIR ROUTE MAPS : AARON KOBLIN
  • 48. Before we start maps Networks Références PARTIAL CONCLUSION Maps are useful
  • 49. Before we start maps Networks Références PARTIAL CONCLUSION Maps are useful There are rules (over representation of large area, colorpleth,etc..)
  • 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. 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. 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. 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. 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. 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. 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. 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. 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. Before we start maps Networks Références IS THIS A NETWORK OR A MAP ?
  • 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. 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. Before we start maps Networks Références TYPES OF NETWORKS Adjacency Networks
  • 63. Before we start maps Networks Références TYPES OF NETWORKS Adjacency Networks
  • 64. Before we start maps Networks Références TYPES OF NETWORKS Adjacency Networks Bipartite Networks
  • 65. Before we start maps Networks Références TYPES OF NETWORKS Adjacency Networks Bipartite Networks
  • 66. Before we start maps Networks Références TYPES OF NETWORKS Adjacency Networks Bipartite Networks From bipartite, one can create adjacency
  • 67. Before we start maps Networks Références TYPES OF NETWORKS Adjacency Networks Bipartite Networks From bipartite, one can create adjacency
  • 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. Before we start maps Networks Références TYPES OF NETWORKS Undirected (A& B ’know each other’) and directed (A ’owe money to’ B)
  • 70. Before we start maps Networks Références TYPES OF NETWORKS Undirected (A& B ’know each other’) and directed (A ’owe money to’ B)
  • 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. Before we start maps Networks Références FAMILIES OF GRAPHS
  • 73. Before we start maps Networks Références FAMILIES OF GRAPHS Complete (top left) ; ring (top right) ; tree (bottom left) ; and star (bottom right)
  • 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. 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. 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. 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. 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. Before we start maps Networks Références A CLASSICAL NETWORK EXAMPLE Relationships of all of Victor Hugo’s characters of "Les Miserables".
  • 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Before we start maps Networks Références FORCE-DIRECTED GRAPHS : Physically-based model + aesthetic touch + fuzzy stuff... From Tunkelang (1998)
  • 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. 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. 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. 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. 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. 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. 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. 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. Before we start maps Networks Références LAYOUT AESTHETICS From Katya Ognyanova (2015)
  • 102. Before we start maps Networks Références A NOTE ON DATA : MATRIX From Katya Ognyanova (2015)
  • 103. Before we start maps Networks Références A NOTE ON DATA : EDGE LIST From Katya Ognyanova (2015)
  • 104. Before we start maps Networks Références A NOTE ON DATA : NODE LIST From Katya Ognyanova (2015)
  • 105. Before we start maps Networks Références A NOTE ON DATA : NODE ATTRIBUTES From Katya Ognyanova (2015)
  • 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. Before we start maps Networks Références NETWORKS : ADJACENT MATRIX PLOT As in many example, sorting is very useful !
  • 108. Before we start maps Networks Références NETWORKS : ADJACENT MATRIX PLOT Adjacent matrix help identifying patterns From McGuffin (2012)
  • 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. Before we start maps Networks Références NETWORKS : ARC DIAGRAM PLOT Now we can sort ! (according to groups, and then degrees) FromGaston Sanchez
  • 111. Before we start maps Networks Références NETWORKS : ARC DIAGRAM PLOT : MY TOUCH !
  • 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. 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. 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. Before we start maps Networks Références PROBLEM 1 : IDENTIFY REFERENCE GRAPHS Random Graphs difficult to identify
  • 116. Before we start maps Networks Références PROBLEM 1 : IDENTIFY REFERENCE GRAPHS Random Graphs difficult to identify
  • 117. Before we start maps Networks Références PROBLEM 1 : IDENTIFY REFERENCE GRAPHS Random Graphs difficult to identify Or
  • 118. Before we start maps Networks Références PROBLEM 1 : IDENTIFY REFERENCE GRAPHS Random Graphs difficult to identify Or
  • 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. Before we start maps Networks Références PROBLEM 2 : LAYOUT CHANGE PERCEPTION Three layouts of the same graph From Kolaczyk and Csárdi (2014)
  • 121. Before we start maps Networks Références PROBLEM 2 : LAYOUT CHANGE PERCEPTION Three layouts of the same graph From Kolaczyk and Csárdi (2014)
  • 122. Before we start maps Networks Références PROBLEM 2 : LAYOUT CHANGE PERCEPTION Three layouts of the same graph From Kolaczyk and Csárdi (2014)
  • 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. Before we start maps Networks Références PROBLEM 3 : DECORATION HELPS ? From Kolaczyk and Csárdi (2014)
  • 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. 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. 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. 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. Before we start maps Networks Références SOLUTIONS : SPLIT ? From Kolaczyk and Csárdi (2014)
  • 130. Before we start maps Networks Références SOLUTIONS : SPLIT ? Two ego-centric views of the karate club network From Kolaczyk and Csárdi (2014)
  • 131. Before we start maps Networks Références SOLUTIONS : AGGREGATE ? From Kolaczyk and Csárdi (2014)
  • 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. 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. 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.