ABSTRACT
Within the remit of `Data for Development' there have been a number of promising recent works that investigate the use of mobile phone Call Detail Records (CDRs) to estimate the spatial distribution of poverty or socio-economic status. The methods being developed have the potential to offer immense value to organisations and agencies who currently struggle to identify the poorest parts of a country, due to the lack of reliable and up to date survey data in certain parts of the world. However, the results of this research have thus far only been presented in isolation rather than in comparison to any alternative approach or benchmark. Consequently, the true practical value of these methods remains unknown. Here, we seek to allay this shortcoming, by proposing two baseline poverty estimators grounded on concrete usage scenarios: one that exploits correlation with population density only, to be used when no poverty data exists at all; and one that also exploits spatial autocorrelation, to be used when poverty data has been collected for a few regions within a country. We then compare the predictive performance of these baseline models with models that also include features derived from CDRs, so to establish their real added value. We present extensive analysis of the performance of all these models on data acquired for two developing countries -- Senegal and Ivory Coast. Our results reveal that CDR-based models do provide more accurate estimates in most cases; however, the improvement is modest and more significant when estimating (extreme) poverty intensity rates rather than mean wealth.
- J. C. Aker and I. M. Mbiti. Mobile Phones and Economic Development in Africa. Journal of Economic Perspectives, 24(3):207--232, 2010. Google ScholarCross Ref
- L. Anselin. Local indicators of spatial association lisa. Geographical Analysis, 27(2):93--115, 1995. Google ScholarCross Ref
- D. Balcan, V. Colizza, B. Gonçalves, H. Hu, J. J. Ramasco, and A. Vespignani. Multiscale mobility networks and the spatial spreading of infectious diseases. Proceedings of the National Academy of Sciences of the United States of America, 106(51):21484--9, Dec. 2009. Google ScholarCross Ref
- A. Barrat, M. Barthélemy, R. Pastor-Satorras, and A. Vespignani. The architecture of complex weighted networks. Proceedings of the National Academy of Sciences of the United States of America, 101(11):3747--52, Mar. 2004. Google ScholarCross Ref
- L. M. A. Bettencourt, J. Lobo, D. Helbing, C. Kühnert, and G. B. West. Growth, innovation, scaling, and the pace of life in cities. Proceedings of the National Academy of Sciences, 104(17):7301--7306, 2007. Google ScholarCross Ref
- V. D. Blondel, M. Esch, C. Chan, F. Clerot, P. Deville, E. Huens, F. Morlot, Z. Smoreda, and C. Ziemlicki. Data for Development: the D4D Challenge on Mobile Phone Data. page 10, Sept. 2012.Google Scholar
- F. Bruckschen, T. Schmid, and T. Zbiranski. Cookbook for a socio-demographic basket. In D4D Challenge Senegal Sessions Scientific Papers, Netmob '15, 2015.Google Scholar
- Y.-A. de Montjoye, Z. Smoreda, R. Trinquart, C. Ziemlicki, and V. D. Blondel. D4d-senegal: The second mobile phone data for development challenge. 2014.Google Scholar
- N. Eagle, M. Macy, and R. Claxton. Network diversity and economic development. Science (New York, N.Y.), 328(5981):1029--31, May 2010. Google ScholarCross Ref
- V. Frias-martinez, V. Soto, J. Virseda, and E. Frias-martinez. Computing Cost-Effective Census Maps From Cell Phone Traces. In Pervasive Urban Applications (PURBA), Newcastle, 2012.Google Scholar
- V. Frias-Martinez and J. Virseda. On the relationship between socio-economic factors and cell phone usage. In Fifth International Conference on Information and Communication Technologies and Development (ICTD '12), New York, New York, USA, Mar. 2012. Google ScholarDigital Library
- V. Frias-Martinez, J. Virseda-Jerez, and E. Frias-Martinez. On the relation between socio-economic status and physical mobility. Information Technology for Development, 18(2):91--106, Apr. 2012. Google ScholarCross Ref
- K. M. M. Gary S. Becker, Edward L. Glaeser. Population and economic growth. The American Economic Review, 89(2):145--149, 1999. Google ScholarCross Ref
- W. Jung and F. Wang. Gravity model in the Korean highway. Europhysics Letters, 81, 2008. Google ScholarCross Ref
- P. Kaluza, A. Kölzsch, M. T. Gastner, and B. Blasius. The complex network of global cargo ship movements. Journal of the Royal Society, Interface / the Royal Society, 7(48):1093--103, July 2010.Google Scholar
- G. Krings, F. Calabrese, C. Ratti, and V. D. Blondel. Urban gravity: a model for inter-city telecommunication flows. Journal of Statistical Mechanics: Theory and Experiment, 2009(07):L07003, May 2009. Google ScholarCross Ref
- H. Mao, X. Shuai, Y.-Y. Ahn, and J. Bollen. Mobile communications reveal the regional economy in cote d'ivoire. In D4D Challenge Book of Abstracts, Netmob, 2013.Google Scholar
- P. Masucci, J. Serras, A. Johansson, and M. Batty. Gravity versus radiation models: On the importance of scale and heterogeneity in commuting flows. Physics Review E, 8(2), August 2013. Google ScholarCross Ref
- P. A. P. Moran. Notes on continuous stochastic phenomena. Biometrika, 37(1/2):17--23, 1950. Google Scholar
- W. Pan, G. Ghoshal, C. Krumme, M. Cebrian, and A. Pentland. Urban characteristics attributable to density-driven tie formation. Nature communications, 4:1961, 2013. Google ScholarCross Ref
- N. Pokhriyal and W. Dong. Virtual networks and poverty analysis in senegal. In D4D Challenge Senegal Sessions Scientific Papers, Netmob '15, 2015.Google Scholar
- C. Smith, A. Mashhadi, and L. Capra. Ubiquitous sensing for mapping poverty in developing countries. In D4D Challenge Book of Abstracts, Netmob, 2013.Google Scholar
- C. Smith-Clarke, A. Mashhadi, and L. Capra. Poverty on the cheap: Estimating poverty maps using aggregated mobile communication networks. In Proceedings of the 32nd annual ACM conference on Human factors in computing systems. ACM, 2014. Google ScholarDigital Library
- V. Soto, V. Frias-Martinez, J. Virseda, and E. Frias-Martinez. Prediction of socioeconomic levels using cell phone records. User Modeling, Adaption and Personalization, pages 377--388, 2011. Google ScholarDigital Library
- C. Viboud, O. N. Bjø rnstad, D. L. Smith, L. Simonsen, M. A. Miller, and B. T. Grenfell. Synchrony, waves, and spatial hierarchies in the spread of influenza. Science (New York, N.Y.), 312(5772):447--51, Apr. 2006. Google ScholarCross Ref
Index Terms
- Beyond the Baseline: Establishing the Value in Mobile Phone Based Poverty Estimates
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