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

Lasantha Fernando · Aparna Surendra · Sriganesh Lokanathan · Thavisha Perera-Gomez

13 citations0/0 files storedInternational Workshop on Data Science for Macro-Modeling (DSMM)

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Abstract

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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References

2003Micro-Level Estimation of Poverty and Inequality 2015Predicting poverty and wealth from mobile phone metadata 2012Predictability of population displacement after the 2010 Haiti earthquake 2017Mapping poverty using mobile phone and satellite data 2016Rapid and near Real-time Assessments of Population Displacement Using Mobile Phone Data Following Disasters: The 2015 Nepal Earthquake 2012On the relationship between socio-economic factors and cell phone usage 2014Poverty on the cheap 2014Estimating Food Consumption and Poverty Indices with Mobile Phone Data

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