Advancing the spatial analysis of poverty in developing countries combining different data sources

The availability of granular and timely information is key to design, target, monitor, and assess policies contributing to reaching the SDGs. Census and survey data remain fundamental but relying solely on these traditional sources of data presents clear challenges and limitations. How do we measure and monitor development outcomes in intercensal years and for countries where census data are unreliable and outdated? How do we monitor progress at sub-national scales for which household surveys are not representative? In this seminar, Ms Jessica Steele, University of Southampton, will provide an overview of geospatial methods that can help overcome these challenges, drawing on ‘big data’ and other non-traditional data sources. She will present examples from the WorldPop project to discuss poverty mapping in developing countries. The seminar will focus on three types of data– geolocated household surveys, satellite-derived and GIS data, and mobile phone data, along with the challenges related to their acquisition, processing and analysis.

SPEAKER:

Jessica Steele is a Senior Fellow within the WorldPop project at the University of Southampton’s Department of Geography and Environmental Science. Ms Steele leads projects on data and methods for estimating dynamic populations and movement, intraurban population mapping, and poverty mapping in LMICs. She holds a PhD in Geography with a specialisation in advanced remote sensing. Her research interests include applied spatial modelling, geostatistics, GIS and remote sensing, mobile phone data, population modelling, and poverty mapping. Ms Steele has provided technical advice on these themes to a range of governments; UN agencies (including UNDP and UNICEF); multilateral/regional organisations (including the World Bank and the Inter-American Development Bank); telecom companies (MTC Namibia, Telenor, Ncell); and a range of UK and global universities/research organisations. She currently leads a Gates funded project on mapping seasonal denominators of population densities in LMICs using data from mobile phones, Baidu, and Google location histories, along with ancillary data produced from satellites and voluntary geographic GIS information.