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Correcting for Sample Bias with Application to the Case of Senegal

This paper sets out to explain modeling and correcting sample bias in Call Detail Records (CDRs). A proper understanding of sample bias is key to producing useful estimates derived from CDRs: such calculations rely heavily on a good understanding of how the sample (cell-phone users) relates to the larger populations it is drawn from. It could have major applications in crisis monitoring and response, as in the case of flood vulnerability predictions. Data-Pop Alliance uses both statistical and machine learning approaches, relying on data from Orange’s D4D challenges, official censuses and Demographic and Health Survey (DHS) program data.

Correcting for Sample Bias with Application to the Case of Senegal

Authors:
Emmanuel Letouzé, Gabriel Pestre, Emilio Zagheni, Espen Beer Prydz

November 2015

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