Abstract
The mapping of populations socio-economic well-being is highly constrained by the logistics of censuses and surveys. Consequently, spatially detailed changes across scales of days, weeks, or months, or even year to year, are difficult to assess; thus the speed of which policies can be designed and evaluated is limited. However, recent studies have shown the value of mobile phone data as an enabling methodology for demographic modeling and measurement. In this work, we investigate whether indicators extracted from mobile phone usage can reveal information about the socio-economical status of microregions such as districts (i.e., average spatial resolution \({<}2.7\) km). For this we examine anonymized mobile phone metadata combined with beneficiaries records from unemployment benefit program. We find that aggregated activity, social, and mobility patterns strongly correlate with unemployment. Furthermore, we construct a simple model to produce accurate reconstruction of district level unemployment from their mobile communication patterns alone. Our results suggest that reliable and cost-effective economical indicators could be built based on passively collected and anonymized mobile phone data. With similar data being collected every day by telecommunication services across the world, survey-based methods of measuring community socioeconomic status could potentially be augmented or replaced by such passive sensing methods in the future.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Bandicoot can be found at http://bandicoot.mit.edu/docs/.
References
Aleissa, F., Alnasser, R., Almaatouq, A., Jamshaid, K., Alhasoun, F., González, M.C., Alfaris, A.: Wired to connect: analyzing human communication and information sharing behavior during extreme events. In: KDD Workshop on Learning about Emergencies from Social Information (2014)
Alhasoun, F., Almaatouq, A., Greco, K., Campari, R., Alfaris, A., Ratti, C.: The city browser: utilizing massive call data to infer city mobility dynamics. In: SIGKDD International Workshop on Urban Computing (2014)
Almaatouq, A.: Complex systems and a computational social science perspective on the labor market. arXiv preprint arXiv:1606.08562 (2016)
Almaatouq, A., Alabdulkareem, A., Nouh, M., Shmueli, E., Alsaleh, M., Singh, V.K., Alarifi, A., Alfaris, A., Pentland, A.S.: Twitter: who gets caught? Observed trends in social micro-blogging spam. In: Proceedings of the 2014 ACM Conference on Web Science, pp. 33–41. ACM (2014)
Almaatouq, A., Radaelli, L., Pentland, A., Shmueli, E.: Are you your friends friend? Poor perception of friendship ties limits the ability to promote behavioral change. PloS One 11(3), e0151588 (2016)
Becker, G.S.: The Economic Approach to Human Behavior. University of Chicago Press, Chicago (1976)
Bettencourt, L.M., Lobo, J., Helbing, D., Kühnert, C., West, G.B.: Growth, innovation, scaling, and the pace of life in cities. Proc. Natl. Acad. Sci. 104(17), 7301–7306 (2007)
Blumenstock, J., Cadamuro, G., On, R.: Predicting poverty and wealth from mobile phone metadata. Science 350(6264), 1073–1076 (2015)
Caldwell, D.F., Burger, J.M., et al.: Personality characteristics of job applicants and success in screening interviews. Pers. Psychol. 51(1), 19–136 (1998)
Eagle, N., Macy, M., Claxton, R.: Network diversity and economic development. Science 328(5981), 1029–1031 (2010)
Eagle, N., Pentland, A.: Reality mining: sensing complex social systems. Pers. Ubiquit. Comput. 10(4), 255–268 (2006)
Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.L.: Understanding individual human mobility patterns. Nature 453(7196), 779–782 (2008)
Granovetter, M.: Economic action and social structure: the problem of embeddedness. Am. J. Sociol. 91, 481–510 (1985)
Granovetter, M.S.: The strength of weak ties. Am. J. Sociol. 78, 1360–1380 (1973)
Henrich, J., Boyd, R., Bowles, S., Camerer, C., Fehr, E., Gintis, H., McElreath, R.: In search of homo economicus: behavioral experiments in 15 small-scale societies. Am. Econ. Rev. 91, 73–78 (2001)
John, O.P., Srivastava, S.: The big five trait taxonomy: history, measurement, and theoretical perspectives. In: Handbook of Personality: Theory and Research, 1999, vol. 2, pp. 102–138 (1999)
Kohonen, T.: The self-organizing map. Neurocomputing 21(1), 1–6 (1998)
Krueger, A., Mas, A., Niu, X.: The evolution of rotation group bias: will the real unemployment rate please stand up? Technical report, National Bureau of Economic Research (2014)
Lazer, D., Pentland, A.S., Adamic, L., Aral, S., Barabasi, A.L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., et al.: Life in the network: the coming age of computational social science. Science 323(5915), 721 (2009). (New York, NY)
Llorente, A., Garcia-Herranz, M., Cebrian, M., Moro, E.: Social media fingerprints of unemployment. Plos One 10(5), e0128692 (2015). http://dx.doi.org/10.1371%2Fjournal.pone.0128692
Montjoye, Y.-A., Quoidbach, J., Robic, F., Pentland, A.S.: Predicting personality using novel mobile phone-based metrics. In: Greenberg, A.M., Kennedy, W.G., Bos, N.D. (eds.) SBP 2013. LNCS, vol. 7812, pp. 48–55. Springer, Heidelberg (2013). doi:10.1007/978-3-642-37210-0_6
Nielsen, H.B., Lophaven, S.N., Sondergaard, J.: Dace, a matlab kriging toolbox. Technical University of Denmark, DTU, Informatics and mathematical modelling. Lyngby-Denmark (2002)
Pan, W., Ghoshal, G., Krumme, C., Cebrian, M., Pentland, A.: Urban characteristics attributable to density-driven tie formation. Nat. Commun. 4, 1–35 (2013)
Pappalardo, L., Vanhoof, M., Gabrielli, L., Smoreda, Z., Pedreschi, D., Giannotti, F.: An analytical framework to nowcast well-being using mobile phone data. Int. J. Data Sci. Anal., 1–18 (2016). Springer
Pentland, A.: Social Physics: How Good Ideas Spread-The Lessons from a New Science. Penguin, New York (2014)
Phithakkitnukoon, S., Smoreda, Z., Olivier, P.: Socio-geography of human mobility: a study using longitudinal mobile phone data. Plos One 7(6), e39253 (2012). http://dx.doi.org/10.1371%2Fjournal.pone.0039253
Schneider, C.M., Belik, V., Couronné, T., Smoreda, Z., González, M.C.: Unravelling daily human mobility motifs. J. R. Soc. Interface 10(84), 20130246 (2013)
Song, C., Koren, T., Wang, P., Barabási, A.L.: Modelling the scaling properties of human mobility. Nat. Phys. 6(10), 818–823 (2010)
Toole, J.L., Lin, Y.R., Muehlegger, E., Shoag, D., González, M.C., Lazer, D.: Tracking employment shocks using mobile phone data. J. R. Soc. Interface 12(107), 20150185 (2015)
Wehrens, R., Buydens, L.M., et al.: Self-and super-organizing maps in R: the Kohonen package. J. Stat. Softw. 21(5), 1–19 (2007)
Welch, W.J., Buck, R.J., Sacks, J., Wynn, H.P., Mitchell, T.J., Morris, M.D.: Screening, predicting, and computer experiments. Technometrics 34(1), 15–25 (1992)
Acknowledgments
The authors thank the Center for Complex Engineering Systems (CCES) at KACST and MIT and the Media Lab at MIT for their support.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Almaatouq, A., Prieto-Castrillo, F., Pentland, A. (2016). Mobile Communication Signatures of Unemployment. In: Spiro, E., Ahn, YY. (eds) Social Informatics. SocInfo 2016. Lecture Notes in Computer Science(), vol 10046. Springer, Cham. https://doi.org/10.1007/978-3-319-47880-7_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-47880-7_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47879-1
Online ISBN: 978-3-319-47880-7
eBook Packages: Computer ScienceComputer Science (R0)