Abstract
Identifying socioeconomic status for a given geographical region is important for a better ruling and policy making of a country. This is a significant fact when both government and private sector organizations are implementing various schemas for the well-being of people. For example, telecommunication providers, the main stakeholders of this project need to identify behavioural patterns of its user base to accurately target the relevant users when deploying advertising campaigns and other promotional activities. We propose a prediction model to classify each geographical region in Sri Lanka into a particular socioeconomic status using CDR data. This will ease the process of classification of socioeconomic status of geographical regions unlike the traditional methods like census and household surveys, which require a lot of time and money along with many other resources.
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Acknowledgements
The authors would like to express their heartiest gratitude towards Mr. Nisansa de Silva for giving us advice and helping us through the problems we faced during the research. Moreover, they would like to thank Dr. Shehan A. Perera for sharing his expertise knowledge in the field and spending his valuable time to guide us through the research.
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Wijesinghe, W.O.K.I.S., Kumarasinghe, C.U., Mannapperuma, J., Liyanage, K.L.D.U. (2020). Socioeconomic Status Classification of Geographic Regions in Sri Lanka Through Anonymized Call Detail Records. In: Haldorai, A., Ramu, A., Mohanram, S., Onn, C. (eds) EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-19562-5_30
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DOI: https://doi.org/10.1007/978-3-030-19562-5_30
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