DataFeed

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.

Topics

Author(s)

Author(s): 
Emmanuel Letouzé, Gabriel Pestre, Emilio Zagheni, Espen Beer Prydz

Partner Organization(s)

Data Pop Alliance

Share

Recomendations

Annual Report

Overview and Outlook 2022-2024

10 YEARS IN REVIEW: A LETTER FROM OUR DIRECTOR Finding appropriate metrics

Project Report

Review of Technology-Based Interventions to Address Child Marriage and Female Genital Mutilation

The need to end child marriage and FGM with innovative and impactful

Journal Article

Using Facebook Advertising Data to Describe the Socio-Economic Situation of Syrian Refugees in Lebanon

While the fighting in the Syrian civil war has mostly stopped, an

Project Report

Movilidad para llegar más lejos: ¿cómo se mueven las mujeres en Lima y CDMX?

El presente estudio analiza los patrones de movilidad de las mujeres de

Compilation

Links We Like: A Compilation of 40 Editions

Links We Like Turns 40! Technically, Links We Like (LWL) has been