1. Title Mobility, Migration, and Mobile Phones Amy Wesolowski The Santa Fe Institute & Carnegie Mellon University 30 June 2010

2. Mobile Phones

3. March 2009: 291 million subscribers in Africa (30%) April 2010: penetration near 94% in urban Africa Expected 2012: 561 million subscribers in Africa (54%)

4. Data

5. Data provided by Safaricom Data from June 2008 - June 2009 (minus February 2009) Look at data month to month +11,000 unique cell towers in Kenya ~ 2,700 cell tower locations ~ 12 billion calls ~ 11 million subscribers ** anonymized IDs

6. Call Records: Caller | Called | Date | Duration | Tower - Caller | Tower - Called | Adoption Information: Caller | Date of Activation | Services Adopted | Payment Information: Caller | Top - Up Value | Date | Tower - Caller | Sambaza/M-Pesa: Caller | Called | Amount Transferred | Date | Tower - Caller | Tower - Called |

7. Degree

8. Living Tower

9. Movement Score

10. PNAS Social Interactions, Migration, and Social Integration in Developing Societies Measured Through Mobile Phone Communications Y. Montejoyue, A. Wesolowski, N. Eagle, and L. Bettencourt, “Social Interactions, Migration, and Social Integration in Developing Societies Measured Through Mobile Phone Communications”, PNAS, submitted May 2010.

11. How does social integration correlate with migration? When individuals move to a new place, how does their degree to their home versus their degree to their new region influence migrating back home?

12. Consider: Migrants in Rwanda and Kenya Data: Rwanda (4 years, 1.5 million total callers) Kenya (1 year, 10 million total callers) Define: Back movers, permanent movers Analyze: Degree to new region vs. Degree to home region

13. Rural to Capital. Rwanda. Example Back Movers: Individual lives in rural Rwanda for 6 months, then moves to Kigali for 6 months, then moves back to rural Rwanda for 6 months Permanent Movers: Individual lives in rural Rwanda for 6 months, then moves to Kigali for the rest of the year Degree to New Region: Kigali Degree to Home Region: Rural Rwanda

17. 2009: 95% of urban growth occurring in developing countries 2015: 500 cities whose population is over one million 2050: urban population will reach 10 billion

19. Book Chapter Mobile Phones as a Lens Into Slum Dynamics Amy Wesolowski and Nathan Eagle, “Mobile Phones as a Lens Into Slum Dynamics”. Online Research Methods for Urban Planning and Policy, Forthcoming.

20. Background: Cities. Slums. Migration. Kenya Dynamics: Economic. Social. Political Planning and Policy: Upgrading. Resettlement. Health

21. Lack of basic services Substandard housing or illegal and inadequate building structures Overcrowding and high density Unhealthy living conditions and hazardous locations Insecure tenure; irregular or informal settlements Poverty and social exclusion Minimum settlement size

22. 2009: almost 1 billion slum dwellers 2009: 1 in every 3 urban residents lives in a slum 2008: 62% of city dwellers in sub- Saharan Africa live in a slum

24. 2006: 600,000 people or 1/5 of Nairobi’s population live in Kibera 2006: Kibera grows at a rate of 12% 2008: 2 million people in Nairobi live in one of 66 slums/informal settlements

26. Inferring Status

27. How can we develop sociological measures using mobile phone data? Can we infer a measure of ‘status’ using mobile phone communication patterns? Can we observe changes in status? Can we validate our measure using traditional methods?

28. SES. Example For a given individual x, SES(x) = a(top-up(x)) + b(call-volume(x)) + c(degree(x)) + d(sambaza(x)) + e(ms(x)) where a, b, c, d, and e are constants.

30. Economic Dynamics

31. How can we quantify economic changes of individuals? When individuals migrate to a new region, how does their status change? Are there general trends for parts of Kenya? How do slum dwellers compare to other migrants to Nairobi? Can we quantify and identify economic springboards?

32. Springboard Score. Example 1. Associate every individuals with a living tower 2. For every living tower, calculate a springboard score: Delta SES = percent change in SES(x)

36. Migratory Dynamics

37. What are migration trends in Kenya? Where are individuals migrating from/to? How can we better understand the flow of individuals between Kenya? Can we quantify this movement on a finer scale than census data?

38. Voronoi Cells: Given a set of vertices S = {s1,s2, …, sN} on a plane, each cell Y_si has all other points {y} on the plane such that y in {y} is closer to si than other element of S.

40. Malaria Modeling

42. Tribal Dynamics

43. How can we use communication data to understand tribal affiliations? Where are Kibera residents most connected to? What regions of Kenya are Kibera residents calling?

46. Social Dynamics

47. How can we better understand the effects of social insularity? How is an individual’s degree to the slum correlated with SES? Does a higher degree to other slum contacts correspond with a higher SES or lower SES?

50. Resettlement/Upgrading

52. Upgrading: Sanitation. Water. Infrastructure. Social Services. Economics Resettlement: Decanting sites. Low Income Housing

53. How can we use mobile phone data to develop better slum policies and urban planning? Where do people move to after they leave a slum? What are characteristics of ‘successful’ slum dwellers? Where do individuals succeed? How can we design better resettlement/upgrading strategies using actual human behavior?

57. What Happens After They Move?

59. Successful Slum Migrants

62. Future Projects … txt Eagle … … South Africa, India, DR, etc. … … Modeling … … Public Health … … Planning and Policy …

63. The Santa Fe Institute The National Science Foundation The Maine Space Grant Consortium Dave Feldman - SFI, COA Ginger Richardson - SFI Nathan Eagle - SFI, MIT

64. Questions