5-hour Workshop about network mapping and data storytelling. This includes examples about data, networks, visualization, etc. Given on Jan 31st, 2013 during a lecture in the Master Information, Technology and Territories in the Institute of Geography and Social Sciences, Toulouse 2 University. France. Many thanks to @graphcommons for the inspiration.

1. DATA & VISUALIZATION
INVESTIGATING
Clément
COMPLEX TERRITORIES Renaud
Toulouse 2 University - Oct 2013 @clemsos

2. ABOUT ME
Clément Renaud
phD Social networks and urban
spaces in China
Co-founder Sharism Lab #
@sharismlab data
sharismlab.com journalism
visualization
@clemsos social networks
clement.renaud@gmail.com urbanity
www.clementrenaud.com China

3. TODAY CLASS: OBJECTIVES
What is data and Achievement
how people use it
MAP A NETWORK
How it relates to
territories
1) Identify a case
Showcase some
examples 2) Find data you need
Introduce tools 3) Map it

4. TODAY CLASS: DETAILS
Data Morning Duration:
2x3h
Some definitions
Data & territories
Workshop: Network mapping Materials:
Slides (here)
Notes
Viz Afternoon
Visualization
Tools gallery
Workshop: Visualize it

5. DATA,
NETWORKS, PART 1 :
Definitions
ETC.

6. WHAT IS DATA?
DATA :
- Factual information, especially information organized
for analysis or used to reason or make decisions
- Information output by a sensing device or organ that
includes both useful and irrelevant or redundant
information and must be processed to be meaningful
- Information in numerical form that can be digitally
transmitted or processed
F r o m M e r r i a m - We b s t e r

7. SOME
DATA
Look at those

8. DATA=DIGITAL INFORMATION
 Written info in huge amounts
 Quantification of its subject / object
 Storage - in computers databases
 Can be processed by machines
 Huge trend in early 21st Century:
business, ad, indsutry, science, etc.

9. WHAT IS AT STAKE WITH DATA?
Objectivize things to provide new
understanding
A « facts are sacred » approach
towards complex questions and
problem-solving
Apply scientific method to
interrogate any kind of beings,
objects, ideas, etc.

10. HANDLE COMPLEXITY
Linked Data Cloud

11. HANDLE COMPLEXITY
Problem:
Most of data is made by machines,
for machines.
How can we access it?
How can we understand it?

12. THE DATA
SCIENCE
METHOD
From raw data
1. Statistics: Studying to images
2. Data Munging: Suffering
3. Visualization: Storytelling
Mike @dataspora , Sexy Data Geeks. 2009

13. DATA
SCIENCE
Methodology
for
investigation
Examples:
DNA
Brain studies
Social Networks
Analysis
…

14. DATA
JOURNALISM
datajournalism
handbook.org

15. VISUALIZATION
“The brain doesn’t just process information
that comes though the eyes. It also creates
mental visual images that allow us to
reason and plan actions that facilitate
survival.”
A. Cairo, The Functional Art - 2013

16. NETWORKS STRUCTURE
http://www.aaronkoblin.com/work/flightpatterns/

17. LINKEDIN
NETWORK
Vi s u a l i z a t i o n
of my
professional
n e t wo r k u s i n g
Linkedin Labs
Facebook
n e t wo r k g r a p h
can be
generated
using
N e t vi z z

18. SEATTLE
BAND
MAP
The Seattle
Band Map
explores how
bands from the
Pacific
Northwest are
interconnected
through
personal
relationships
and
collaborations.
h t t p : / / w w w. s e a t t
lebandmap.com/

19. MUSE
Muse is an
i n t e r a ct i ve
vi s u a l i z a t i o n o f
scientific
publications to
e xp l o r e t h e
collaborations
b e t we e n
i n s t i t u ti o n s .
h t t p : // t i l l n a g e l .
c o m / 2010/ 11/m
use/

20. KIVA
MAP
Mapping 2005-
2 0 11 K i va
a c t i vi t y ( m i c r o -
loans and
p a yb a c k )
Vi d e o f r o m
h t t p : // vi m e o . c o
m/28413747

21. NETWORK MAPPING PART 2 :
WORKSHOP

22. SURROUNDED BY NETWORKS
The model of a network is everywhere :
cities, DNA, social relationships, Internet,
etc.
Question is : « What connects? » - and how.
What is this strange relationship that links
data to networks?

23. IDENTIFY A NETWORK
Questions:
MAP THE CLASSROOM AS A What is the
NETWORK structure of the
network?
What are the
WHAT CONNECTS different kinds
of data we can
IN THIS identify?
CLASSROOM? How is the data
produced?
exchanged?

24. MAP YOUR OWN NETWORK !
Civil Society NGO-STK-Network Workshop in Istanbul by @graphcommons

25. CONNECTIONS
 Transmission Networks
Something actually flows.
 Interaction Networks
Connection is an event, with a specific
time.
 Attribution Networks
Connection is an expression of a
relationship.
 Affiliation Networks
Connection is a belonging to a group or
category.

26. MAP YOUR OWN NETWORKS
Objectives
Identify an interesting network related to a specific
territory
Ex: Food waste in Toulouse, actors in job research,
etc.
Deliveries
Draw an extensive map of this network
Use colors, dots, line, weight to represent things

27. SOME DIRECTIVES
Where to start a graph?
 You can start with the first thing that comes to your mind, then
grow and tweak the map step by step from there.
Where to stop a graph?
 Putting a definitive graph title and considering only the strong
connections help to limit your network map's scope.
 Connection w eight
Strong connections bring closer the two end nodes, and reveal
tight clusters. In fact, strong ties are more transitive than weak
ties.
 Collaborative mapping
more fruitful and complete graphs, in fact, it is great for brain
storming
From http://graphcommons.com

28. DATA,
IMAGES
GRAPHIC
STORIES
AND TERRITORIES

29. What is the story you want DATA
to tell us? VISUALIZA
TION
Te l l yo u r s t o r y
What is the
specific focus
The example of the Arab yo u wa n t t o
take out of this
Spring data set?

30. EVENTS
TIMELINE
Arab spring: an
i n t e r a ct i ve
timeline of
Middle East
protests
S e e l i ve o n
the Guardian

31. THE
REVOLUTIONS
WERE
TWEETED
I n f o r ma t i o n
F l o ws D u r i n g
t h e 2 0 11
Tu n i s i a n a n d
E g yp t i a n
R e vo l u t i ons
h t t p : // www. d a n
a h . o r g / p r o j e c ts
/IJOC-
ArabSpring/

32. NEWSPAPER
ANALYSIS
Spanish front
page
n e ws p a p e r
a n a l ys i s d u r i n g
the Arab
S p r i ng
h t t p : // www. i e c a
h . o r g / we b / vi s u
al/egipto-libia-
s i r i a - ot r os . ht m

33. WIKIPEDIA
EDITS
Wikipedia
Edits During
the Middle-
E a s t P r o t e s ts
h t t p : // www. yo ut
u b e . c o m/ wa t c h
? v= z 3 Wo 2 2 j l 4 A
c

34. IS THIS
THE SAME
STORY?
Identify
d i ff e r e n c e s
and common
p o i n t s?
W h at a r e k e y
elements to
s u c c e ss i n
each piece?
How has the
data been
produced?

35. OTHER RECENTS EVENTS
Sandy storm - Power Cut Blackout map based on Tweets by Social Flow

36. IMPORTANCE OF DATA LOCALLY
 Crisis management
 Urban planning
 Transportation
 Transparency
 Participation
 Recollection
 Space design
 Coverage
 Etc.
NYC Subway Map Update

37. ABOUT OPEN DATA
 Made publicly  Open government
available (release, initiative
access,  Code for America
documentation…)  US political tradition
 Open Data is not based on
only governmental accountability
data  Obama campaign has
 Mutual economic
interests
http://www.data.gov/

38. OPEN DATA IN FRANCE
OpenData 71 Rennes Collective Action
Top-down initiative Citizen-based
Public funding No funding
National target Local target
Cons Cons
Nobody use this data Illegal practices
Unsustainable Unclear program
Pros Pros
Nice data platform Data is in use

39. BASICS
VISUALIZATION &
METHODS

40. WORKFLOW: CREATE A DATAVIZ
 Objectives: extract, process, visualize, publish
 Tools : Web-based, softwares, languages
Ben Fry, Computational Design. 2004

41. DEFINE A DATAVIZ PROJECT
You may find data in
weird places.
A story tends to Draft, draft, draft
1. begin somewhere Chose your tools
based on your
2. tell something (team) skills.
3. end. Mind the time
spent!
These apply for a map, a graph, a Be kind to your
visualization, etc. readers

42. STEP BY STEP
How it should work: How it really works
1. Great, I have some nice
1. Define project data/a brilliant idea !
2. Let’s try some tools
2. Find data 3. Well, I just waste 3
hours on tutorials
3. Draft something visual 4. I should do something
4. Define tools & time easier
5. Another 2 hours on
5. Clean and refine data google
6. What was this brilliant
6. Visualize idea again?
7. I should post this link
7. Publish on Facebook
8. It’s late already. Let’s
8. Promote just forget about this
dataviz thing….

43. TOOLS ARE EVIL.
GEEK
GALLERY

44. WEB-BASED:
GOOGLE FUSION TABLES
Easy maps & graph Example

45. WEB-BASED:
INFOGR.AM
http://infogr.am

46. SOFTWARE:
ADOBE ILLUSTRATOR
Graphic design and vectors
http://www.informationisbeautiful.net/

47. SOFTWARE:
TILEMILL
Draw Beautiful Maps
http://mapbox.com/tilemill/gallery/

48. SOFTWARE:
GEPHI
Photoshop for Network Graph

49. LANGUAGE:
R
Statistics on Steroids

50. LANGUAGE:
PROCESSING
Interactive Awesomeness

51. DATA STORYTELLING WORKSHOP
IN PRACTICE

52. DESIGN A VISUALIZATION!
Based on your network map, imagine a specific story
you want to tell or a specific idea you want to
investigate with data.
A story 5 min Presentation
A title
A visualization draft
A list of possible data sources & how to get it
 Where to find interesting?
 Can you access it? If not, imagine a way to get this data
 Licensing, ownership & privacy issues

53. YOUR DATA STORY
You have to put together a 5 min presentation
about your data story
You have to show:
A story
A title
A visualization draft
How do you plan to get your data?
(Some existing data, if possible)

54. COURT OF ATTENDEES
For each presentation, we split the attendees in 2 groups: pros & cons
The groups should change each time (one time pros, one time cons).
Pros Cons
What is so great about Why is this presentation
this presentation? so awful?
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55. THANKS ! SEE YOU
ONLINE
BYE @clemsos