Using nighttime imagery to estimate Gross Domestic Product at sub-national levels in Africa

Authors: Paul Sutton*, University of Denver, Xuantong Wang, Department of Geography & the Environment, University of Denver, Denver, CO 80208 USA, Mickey Rafa, Pardee Center for International Futures, Josef Korbel School of International Studies, University of Denver, Denver, CO 80208 USA, Jonathan Moyer, Pardee Center for International Futures, Josef Korbel School of International Studies, University of Denver, Denver CO 80208
Topics: Remote Sensing, Urban Geography, Spatial Analysis & Modeling
Keywords: GDP Proxy measure, sub-national GDP Estimation, Uganda
Session Type: Paper
Day: 4/12/2018
Start / End Time: 8:00 AM / 9:40 AM
Room: Lafayette, Marriott, River Tower Elevators, 41st Floor
Presentation File: No File Uploaded


Subnational socio-economic data are often difficult to obtain in low-income countries. Gross Domestic Product (GDP) data – and by extension, GDP per capita – do not exist for the 116 districts of Uganda, but there are many ways of constructing estimates with conceptual validity. This paper compares traditional regression-based approaches for estimation with geo-spatial models that use nighttime satellite imagery (from the Defense Meteorological Satellite Program-Operational Line Scanner, DMSP-OLS) and grid-based population density (from Landscan) to allocate Uganda’s national GDP to the district level. This paper also includes district-level data on agricultural production and the market value of associated crops to represent within country variations in agriculture’s contribution to GDP. We apply these methods to all countries in Africa to produce a spatially dis-aggregated map of GDP and GDP per capita at a spatial resolution of 1 km2.

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