The slides of the talk on human mobility and migration with BigData. Speaker Fosca Giannotti ISTI-CNR SoBigData

1. Social Mining & Big Data Ecosystem
Exploring human mobility and
migration with BigData
Research @ SoBigData.eu
Fosca Giannotti, Dino Pedreschi, Alina Sirbu
(ISTI-CNR | University of Pisa)
www.sobigdata.eu
H2020-INFRAIA-2014-2015
Grant Agreement N. 654024

2. SoBigData is…
A Multidisciplinary European Infrastructure for Big Data and Social
Data Mining providing an integrated ecosystem for ethically
sensitive scientific discoveries and advanced applications of social
data mining on the various dimensions of social life, as recorded by
“big data”.

3. Big data “proxies” of social life
Shopping patterns
& lifestyle
Desires, opinions,
sentiments
Relationships
& social ties
Movements

4. The Consortium
Italy United Kingdom Germany Estonia
Finland Switzerland Nederlands
Existing national RI’s to be integrated

5. The pillars for reaching the goal
• a distributed data ecosystem for procurement, access and
curation of big social data
• distributed platform of interoperable, social data mining
methods and associated skills: tools, methodologies and
services for mining, analysing, and visualising complex and
massive datasets
• a community of multidisciplinary scientists, innovators,
public bodies, citizen organizations, SMEs, as well as data
science students at any level of education scientific, brought
together by extensive networking and innovation actions

7. Building the e-infra &
boosting joint research: exploratories
Our first exploratories: i.e. the Research Environments tailored to specific
multidisciplinary domains
• different resources will made be available (and discoverable) by
exploratories: data, methods and results/publications
• We will formulate different driving scenarios:
– societal debates
– societal well-being and economic performance
– city of citizens
– migration studies

8. Big Data for City of Citizens
Personal Mobility, Social + Mobility, Personal Sensing
Exploratory:

9. Big Data for Well Being and Economic Performance
Deprivation Index (in France) predicted with Mobile Phone traces
Exploratory:

10. Big Data for Societal Debates
Polarization, controversy and topic trends on societal debates through social media
Exploratory:

11. Big Data for Migration Studies
Human Migration Flows
Next Exploratory:

12. Ethics and
Security

13. Real time demography

14. SOCIAL COMMUNCATION & WEB
ANALYSIS

15. 7 Billion people
6.9 Billion mobile phones

16. Focus on country-wide CDR data

17. Identifying important locations
 “Personal Anchor Points”
AHAS, R., SILM, S., JARV, O., SALUVEER, E., AND TIRU, M. 2010. Using mobile positioning data to model
locations meaningful to users of mobile phones. Journal of Urban Technology 17, 1, 3–27.

18. Estimating population density
 Sample results on Portugal
A = Census B = GSM data C = Environment/Infrastructures-based
Pierre Deville et al. Dynamic population mapping using mobile phone data.
PNAS vol. 111 no. 45, pp. 15888–15893, doi: 10.1073/pnas.1408439111

19. Estimating population density
 Sample usage: evaluate seasonal changes
 Summer variations vs. Winter period

20. CLASSIFYING CITY USERS
L. Gabrielli, Furletti, B., Trasarti, R., Giannotti, F., and Pedreschi, D., “City users’
classification with mobile phone data”, in IEEE Big Data, Santa Clara (CA) - USA, 2015

21. The Sociometer
Classify city users based on their call behavior profile as recorded
in the Call Data Records.

22. Sociometer: the city user meter
Pisa, January 2012
Analysis of GSM calls data for understanding user mobility behavior
B Furletti, L Gabrielli, C Renso, S Rinzivillo Big Data, 2013 IEEE International Conference on, 550-555

23. The many profiles of an individual
B
Commuter
A
Dynamic Resident

24. Pisa:
In Commuters

25. Pisa:
Out Commuters

26. Static residents GSM validation with
administrative data

27. Validation of inter-city flows
The use of mobile phone data, Prof. Dino Pedreschi – Roma, 10/02/2017

28. Systematic flows

29. Visitor flow

30. COMMUTER NETWORK
VISITOR NETWORK
INTER-CITY FLOWS, WITH SEMANTICS

31. Measuring PRESENCE during big events
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Giovedi Venerdi Sabato Domenica
Presenze
Presence of Visitors GSM - Pisa - Historical Center
Presenze Internet Festival Presenza media settimanale
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Giovedi Venerdi Sabato Domenica
Presenze
Presence of Visitors GSM - Lucca Comics 2012
Presenze Comics 2012 Presenza media settimanale

32. Measuring visitors within a big city
Result for each user: set of individual profiles.

33. San Pietro Square

34. Olympic Stadium

35. Estimating wellbeing with mobility data
AI and Big Data
35
A
B
C
H
W

36. Big Data for Migration Studies
Exploratory:

37. Migration studies
• Use unconventional big datasets (social network, mobile,
publications) to study migration (flows, stocks, impact on
countries of origin and destination)
• Use official datasets to validate results from
unconventional datasets
• Use both types of data to evaluate impact of policies on
migration
• We are starting several projects

38. Big datasets
• Social network and web data
• Twitter Streaming data: various twitter datasets from project partners,
in various languages, with geolocation
• GDELT Knowledge Graph database. a Big Data repertoire of online
news articles.
• Mobile phone data
• Orange dataset: mobile calls between Senegal and the rest of the
world (country to country, 2012).
• Highly educated migrants
• Company data (Estonia and Italy): members of the governing boards
of companies (with place of birth).
• Publication data: DBLP (computer science) and APS (physics)

39. The story: Migration stages
• GO: Understanding migration flows and stocks
• Nowcasting migration through the twitter lens
• Brain-drain and scientific migration
• Policy and illegal migration
• STAY: Evaluating migrant integration
• Sentiment related to migration topics
• Migration and language
• Multi-culturality and sentiment
• Migrant start-uppers
• RETURN: Return of migrants
• Data journalism approach

40. GO
Some examples

41. Migration stocks
• Ongoing analysis
• 3 month geo-localised tweets (august, september, october
2015)
• Estimate user residence
• monthly - country from which the user posted in most days
• Estimate user nationality
• language most used
• Compare with official data

42. GB
IT
Nationalities on twitter
Nationalities on twitter
Official statistics
Official statistics

43. Brain drain
• Question:
• what is the extent of migration of highly education migrants and what is
the effect on the receiving community
• Ongoing Analysis:
• quantify scientific migration in various scientific communities
• evaluate success of immigrants in science
• quantify entrepreneurship of immigrants (company data) and success in
society
• underline achievements of immigrants in an attempt to understand
whether migration is beneficial both for individuals and receiving society

44. Policy and illegal migration
• Question:
• how does policy affect immigration, particularly the ratio between legal
and illegal migrants?
• Planned Analysis:
• quantify legal and illegal migration using official data but also alternative
datasets (roaming data, tweets around hot areas, such as the “jungle of
Calais”)
• identify trend changes
• Challenge 1: identify policy changes and other (economical, historical,
legal) factors that could have cause observed trends
• Challenge 2: identify possible policies to curb illegal migration

45. STAY
Some examples

46. Sentiment on migration topics:
Perception of the Mediterranean Refugee Crisis
• What is the evolution of the discussions about refugees
migration in Twitter?
• What is the sentiment of users across Europe in
relation to the refugee crisis?
• What is the evolution of the perception in countries
affected by the phenomenon?
• Are users more polarised in countries most impacted by
the migration flow?

47. Sentiment on migration topics:
Perception of the Mediterranean Refugee Crisis
• European country mentions Africa & Middle East country mentions
AT-HU border
opens
Flow shift to
Croatia
News about
Syria Terrorist attack in
Nigeria

48. Sentiment on migration topics:
Perception of the Mediterranean Refugee Crisis
• Internal and external perception by country
– Index ρ - the ratio between pro refugees users and against refugees users
– Red means a higher predominance of positive sentiment, higher ρ
– Yellow means a higher predominance of negative sentiment, lower ρ

49. Multiculturality and sentiment
• Question:
• how does migration affect overall sentiment of a community?
• Ongoing Analysis:
• quantify sentiment in tweets coming from different countries
(geolocalised) and in different languages
• compare sentiment of various languages in the same location
• compare sentiment of the same language in various locations across
the world.
• compare sentiment across areas with different levels of immigration

50. RETURN
Some examples

51. Return of migrants
• Demal te niew (Go and Come Back)
• Documentary - interviews with migrants returning to Senegal from Italy
• Featured in Espresso, El Pais
• Presented at Ethnographic Film Festival, Amsterdam and International Day of Migrants, Dakar.
• http://journalismgrants.org/projects/demal-te-niew-go-and-come-back

52. www.SoBigData.eu - Portal

53. • Coordinator
– CNR Fosca Giannotti -
fosca.giannotti@isti.cnr.it
• Co-coordinators
– UNIPI Dino Pedreschi -
dino.pedreschi@di.unipi.it
– USFD The University of
Sheffield, UK
– Kalina Bontcheva -
k.bontcheva@sheffield.ac.uk
• Project Manager
– CNR Valerio Grossi –
vgrossi@di.unipi.it
Project Steering Board

54. Consortium Main Contacts
 CNR Consiglio Nazionale delle Ricerche, Italy Fosca Giannotti - fosca.giannotti@isti.cnr.it
 USFD The University of Sheffield, UK Hamish Cunningham - hamish@gate.ac.uk
 UNIPI Università di Pisa, Italy Dino Pedreschi - dino.pedreschi@di.unipi.it
 FRH Fraunhofer IAIS and IGD, Germany Gennady Andrienko
gennady.andrienko@iais.fraunhofer.de
 UT Tartu Ulikool, Estonia Marlon Dumas - marlon.dumas@ut.ee
 IMT Scuola IMT Lucca, Italy Guido Caldarelli - guido.caldarelli@imtlucca.it
 LUH Leibniz Universitaet Hannover, Germany Wolfgang Nejdl - nejdl@l3s.de
 KCL King’s College London, UK Tobias Blanke - tobias.blanke@kcl.ac.uk
 SNS Scuola Normale Superiore, Italy Fabrizio Lillo - fabrizio.lillo@sns.it
 AALTO Aalto University, Finland Aristides Gionis - aristides.gionis@aalto.fi
 ETHZ ETH Zurich, Switzerland Dirk Helbing - dhelbing@ethz.ch
 TUDelft Technischae Universiteit Delft, Netherlands Jeroen Van Den Hoven -
M.J.vandenHoven@tudelft.nl