DPhil Student
Édith’s research interests lie in the articulation of population, geography and Bayesian statistics through the lens of new data sources such as satellite imagery and digital traces. During her DPhil she will delve into predicting population in data-scarce contexts. A first data-scarce context is geographical and consists in areas impacted by conflicts. A great case study is Sahelian countries where she studies the impact of the multiple recently released built-up maps on population modelling. The second data-scarce context is temporal and consists in intercensal years, that is when census-based figures are becoming obsolete, where she will focus in Colombia and using administrative records and digital traces to nowcast census count. Finally, she is interested in the matters of concern arising from this new co-production of official statistics between statisticians from government, research institutes and international institutions.
She is adamant about making science accessible to the wider public, that is beyond the realm of academic papers, through targeted collaborations with national stats offices and UN agencies, open-access tutorials/scripts and outreach activities.
Alongside LCDS, she is pursuing this research with the lab of Digital and Computational Demography at the Max Planck Institute for Demographic Research (MPIDR) in Germany. Before joining LCDS, she was part of the Spatial Statistical Population Modelling team in the WorldPop Research Group at the University of Southampton developing Bayesian statistical models and applying machine learning approaches to produce high resolution population estimates supporting initiatives of the Bill and Melinda Gates Foundation and the United Nations Population Fund. She earned a MSc degree in applied GIS and remote sensing from the University of Southampton, studying the spatial distribution of deprivation in Kinshasa, Democratic Republic of Congo. Prior to that, she was trained in statistics (MSc from the ENSAE Paristech) and its social sciences application (Master at École Normale Supérieure de Cachan).
Édith Darin
DPhil Student
Édith’s research interests lie in the articulation of population, geography and Bayesian statistics through the lens of new data sources such as satellite imagery and digital traces. During her DPhil she will delve into predicting population in data-scarce contexts. A first data-scarce context is geographical and consists in areas impacted by conflicts. A great case study is Sahelian countries where she studies the impact of the multiple recently released built-up maps on population modelling. The second data-scarce context is temporal and consists in intercensal years, that is when census-based figures are becoming obsolete, where she will focus in Colombia and using administrative records and digital traces to nowcast census count. Finally, she is interested in the matters of concern arising from this new co-production of official statistics between statisticians from government, research institutes and international institutions.
She is adamant about making science accessible to the wider public, that is beyond the realm of academic papers, through targeted collaborations with national stats offices and UN agencies, open-access tutorials/scripts and outreach activities.
Alongside LCDS, she is pursuing this research with the lab of Digital and Computational Demography at the Max Planck Institute for Demographic Research (MPIDR) in Germany. Before joining LCDS, she was part of the Spatial Statistical Population Modelling team in the WorldPop Research Group at the University of Southampton developing Bayesian statistical models and applying machine learning approaches to produce high resolution population estimates supporting initiatives of the Bill and Melinda Gates Foundation and the United Nations Population Fund. She earned a MSc degree in applied GIS and remote sensing from the University of Southampton, studying the spatial distribution of deprivation in Kinshasa, Democratic Republic of Congo. Prior to that, she was trained in statistics (MSc from the ENSAE Paristech) and its social sciences application (Master at École Normale Supérieure de Cachan).