Predicting population-level socio-economic characteristics using Call Detail Records (CDRs) in Sri Lanka

Prior work has shown that mobile network big data can be used as a high-frequency alternative data source to derive proxy measures that have strong predictive capacity to estimate census and poverty data in developing countries. Given that the observations from these studies can be dependent on local context and regional characteristics, we replicate this work targeting two regions in Sri Lanka. We focus on Northern Province, a post-conflict region with a highly vulnerable population and Western Province, an urban region that has been relatively untouched by the conflict. We analyze the relationship between aggregate features related to consumption, social and mobility behaviors derived from pseudonymized mobile phone CDRs and census data associated with population-level socio-economic characteristics. We show that Northern Province exhibits different social and mobility patterns when compared to populations with similar socio-economic characteristics in Western Province, which highlights the importance of replicating prior research studies under different local contexts. We go on to develop predictive models that estimate the census features using the derived CDR features. Our results confirm the applicability of this methodology in a Sri Lankan, post-conflict setting, and highlight potential areas that need to be addressed in order to improve the accuracy of our prediction models.

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Recommended citation: Fernando, L., Surendra, A., Lokanathan, S., & Gomez, T. (2018, June). Predicting population-level socio-economic characteristics using Call Detail Records (CDRs) in Sri Lanka. In Proceedings of the Fourth International Workshop on Data Science for Macro-Modeling with Financial and Economic Datasets (p. 1). ACM.