2016
Journal article
Open Access
An analytical framework to nowcast well-being using mobile phone data
Pappalardo L., Vanhoof M., Gabrielli L., Smoreda Z., Pedreschi D., Giannotti F.
Modeling and Simulation Computer Science - Computers and Society Social and Information Networks (cs.SI) Information Systems Social networks FOS: Physical sciences Complex systems Human mobility Economic development Economic data Forecasting Computers and Society (cs.CY) Computer Science - Social and Information Networks Computational Theory and Mathematics FOS: Computer and information sciences Computer Science Applications Physics - Physics and Society Statistics - Applications Applications (stat.AP) Applied Mathematics Physics and Society (physics.soc-ph) Nowcasting
An intriguing open question is whether measurements derived from Big Data recording human activities can yield high-fidelity proxies of socio-economic development and well-being. Can we monitor and predict the socio-economic development of a territory just by observing the behavior of its inhabitants through the lens of Big Data? In this paper, we design a data-driven analytical framework that uses mobility measures and social measures extracted from mobile phone data to estimate indicators for socio-economic development and well-being. We discover that the diversity of mobility, defined in terms of entropy of the individual users' trajectories, exhibits (i) significant correlation with two different socio-economic indicators and (ii) the highest importance in predictive models built to predict the socio-economic indicators. Our analytical framework opens an interesting perspective to study human behavior through the lens of Big Data by means of new statistical indicators that quantify and possibly "nowcast" the well-being and the socio-economic development of a territory.
Source: International Journal of Data Science and Analytics (Print) 2 (2016): 75–92. doi:10.1007/s41060-016-0013-2
Publisher: Springer
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4. A. Amini, K. Kung, C. Kang, S. Sobolevsky, and C. Ratti. The impact of social segregation on human mobility in developing and urbanized regions. EPJ Data Science, 3, 2014.
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6. A.-L. Barabasi. Linked: The new science of networks. Perseus Publishing, 2002.
7. V. D. Blondel, A. Decuyper, and G. Krings. A survey of results on mobile phone datasets analysis, 2015. cite arxiv:1502.03406.
8. J. Blumenstock. Calling for better measurement: Estimating an individual's wealth and well-being. In ACM KDD (Data Mining for Social Good), 2014.
9. J. Brea, J. Burroni, M. Minnoni, and C. Sarraute. Harnessing mobile phone social network topology to infer users demographic attributes. In Proceedings of the 8th Workshop on Social Network Mining and Analysis, SNAKDD'14. ACM, 2014.
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11. P. Cintia, L. Pappalardo, D. Pedreschi, F. Giannotti, and M. Malvaldi. The harsh rule of the goals: data-driven performance indicators for football teams. In Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, DSAA'15. EEE, 2015.
12. P. J. H. Daas, M. J. Puts, and B. Buelens. Big data and o cial statistics. In The 2013 New Techniques and Technologies for Statistics conference, 2013.
13. A. Decuyper, A. Rutherford, A. Wadhwa, J. Bauer, G. Krings, T. Gutierrez, V. D. Blondel, and M. A. Luengo-Oroz. Estimating food consumption and poverty indices with mobile phone data. CoRR, abs/1412.2595, 2014.
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16. N. Eagle and A. S. Pentland. Eigenbehaviors: identifying structure in routine. Behavioral Ecology and Sociobiology, 63(7):1057{1066, 2009.
17. S. Fortunato. Community detection in graphs. Physics Reports, 486(3-5):75 { 174, 2010.
18. V. Frias-martinez, V. Soto, J. Virseda, and E. Friasmartinez. Can cell phone traces measure social development? In Third Conference on the Analysis of Mobile Phone Datasets, NetMob, 2013.
19. B. Furletti, L. Gabrielli, F. Giannotti, L. Milli, M. Nanni, D. Pedreschi, R. Vivio, and G. Garofalo. Use of mobile phone data to estimate mobility ows. measuring urban population and inter-city mobility using big data in an integrated approach. In 47th SIS Scienti c Meeting of the Italian Statistica Society, Cagliari, 06/2014 2014.
20. F. Galton. Vox populi. Nature, 75(7), 1907.
21. F. Giannotti, D. Pedreschi, A. Pentland, P. Lukowicz, D. Kossmann, J. L. Crowley, and D. Helbing. A planetary nervous system for social mining and collective awareness. EPJ Special Topics, 214:49{75, 2012.
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BibTeX entry
@article{oai:it.cnr:prodotti:367118,
title = {An analytical framework to nowcast well-being using mobile phone data},
author = {Pappalardo L. and Vanhoof M. and Gabrielli L. and Smoreda Z. and Pedreschi D. and Giannotti F.},
publisher = {Springer},
doi = {10.1007/s41060-016-0013-2 and 10.48550/arxiv.1606.06279},
journal = {International Journal of Data Science and Analytics (Print)},
volume = {2},
pages = {75–92},
year = {2016}
}
0000-0003-1208-8321CNR ExploRA
ISTI Repository
DOI
ISTI Repository
DOI
10.1007/s41060-016-0013-2
10.48550/arxiv.1606.06279
arXiv.org e-Print Archive
International Journal of Data Science and Analytics
link.springer.com
