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

Published: 15 June 2018 Publication History

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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cover image ACM Conferences
DSMM'18: Proceedings of the Fourth International Workshop on Data Science for Macro-Modeling with Financial and Economic Datasets
June 2018
66 pages
ISBN:9781450358835
DOI:10.1145/3220547
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 15 June 2018

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Author Tags

  1. large-scale data processing
  2. mobile network big data
  3. regression analysis

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DSMM'18 Paper Acceptance Rate 14 of 17 submissions, 82%;
Overall Acceptance Rate 32 of 64 submissions, 50%

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Cited By

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  • (2024)Rapid poverty estimation using ready-to-use mobile phone data: An application to Côte d’IvoireProceedings of the 7th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies10.1145/3674829.3675061(60-73)Online publication date: 8-Jul-2024
  • (2024)THE OPPORTUNITIES, LIMITATIONS, AND CHALLENGES IN USING MACHINE LEARNING TECHNOLOGIES FOR HUMANITARIAN WORK AND DEVELOPMENTAdvances in Complex Systems10.1142/S021952592440002227:03Online publication date: 3-May-2024
  • (2023)The Spatial Effects of Regional Poverty: Spatial Dependence, Spatial Heterogeneity and Scale EffectsISPRS International Journal of Geo-Information10.3390/ijgi1212050112:12(501)Online publication date: 13-Dec-2023
  • (2022)Mapping urban socioeconomic inequalities in developing countries through Facebook advertising dataFrontiers in Big Data10.3389/fdata.2022.10063525Online publication date: 21-Nov-2022
  • (2022)Analyzing the Social behavior of mobile Subscribers using CDR and Neo4j technology2022 IEEE 2nd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA)10.1109/MI-STA54861.2022.9837745(329-334)Online publication date: 23-May-2022
  • (2021)Analyzing the Behavior and Financial Status of Soccer Fans from a Mobile Phone Network Perspective: Euro 2016, a Case StudyInformation10.3390/info1211046812:11(468)Online publication date: 12-Nov-2021
  • (2021)Estimating urban socioeconomic inequalities through airtime top-up transactions data2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671331(4265-4272)Online publication date: 15-Dec-2021
  • (2021)Predicting cell phone adoption metrics using machine learning and satellite imageryTelematics and Informatics10.1016/j.tele.2021.10162262(101622)Online publication date: Sep-2021
  • (2020)Course models for teaching data scienceJournal of Computing Sciences in Colleges10.5555/3381540.338154535:1(44-56)Online publication date: 30-Jan-2020
  • (2020)Mapping socioeconomic indicators using social media advertising dataEPJ Data Science10.1140/epjds/s13688-020-00235-w9:1Online publication date: 29-Jul-2020
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