1. Mobility diversity and socio-economic development
Luca Pappalardo
@lucpappalard
University of Pisa
ISTI-CNR

2. Surveys have limits
They do not scale to small territories and they are not dynamic
They do not adapt to Pareto distributions:
We cannote use samples
50 people are as wealthy as 3.5 billion people
Young people do not respond to surveys

3. How to measure well-being in an automatic way (without surveys)

4. Big Data
The GPS tracks of car travels originating in Pisa (red) vs. the GPS tracks of car travels originating in Florence (green) (viz by KDD LAB)

5. Advantages of Big Data
They capture the complexity of social systems in their entirety
They allow for the observation of complex phenomena, like diversity, resilience and equality

6. An analytical framework

7. Data format timestamp coords caller callee 04/01 23:45:00
132.56, 23.64
145323
452300
04/02 06:02:00
143.28, 54.22
5602 …

8. Data format timestamp coords caller callee 04/01 23:45:00
132.56, 23.64
145323
452300
04/02 06:02:00
143.28, 54.22
5602 …

9. Orange dataset
20M users
5.7G calls
87K towers days

12. Mobility volume: characteristic distance traveled by an individual
Mobility diversity predictability of an individual’s movements

15. differently diversified

16. Deprivation index Per capita income
- Home location is the most frequent during nighttime (8 pm - 3 am)
- Every individual’s home is assigned to its municipality
- Measures are aggregated at municipality level
- Economic measures are collected
Deprivation index
Per capita income

18. Mobility diversity and well-being
We investigate the correlations between the aggregated mobility measures and the four external socio-economic indicators. We see from the figures that a clear tendency emerges, in this case for mobility entropy: the higher the mobility entropy the lower the deprivation index.

19. Data NM1 NM2 $$ $$ $$ $$ $$ $$ $$$ $$ $ Paris Marseilles Lyon Paris

20. NM1
NM2

21. NM1
NM2

22. Predicting well-being
Multivariate Regression predicting the exact value R2 = 0.42 (deprivation) R2 = 0.25 (income)
Classification: predicting class of well-being (low, medium, high) acc = 0.61 (deprivation) acc = 0.54 (income)

23. Diversity matters
mobility diversity
mobility diversity

24. International Journal of Data Science and Analytics
The purpose of the study I am gonna present you today is to understand the relationships between human mobility and the socio-economic development of a territory. In order to do that we performed a data-driven study in France exploiting the access to mobile phone data provided by the Orange Telecom provider.
International Journal of Data Science and Analytics

25. Take home message(s)
Big Data allow for the observation of complex phenomena (e.g. human mobility)
Mobility diversity is linked to aspects of well-being
A city’s well-being can be predicted from measures extracted from Big Data

26. Grazie! Luca Pappalardo University of Pisa ISTI-CNR
@lucpappalard
University of Pisa
ISTI-CNR