This project resulted in two reports, "Estimating District GDP in Uganda" and "Estimation and Mapping of Sub-National GDP in Uganda Using NPP-VIIRS Imagery."
Estimating District GDP in Uganda: This brief introduces district-level GDP and GDP per capita estimates for Uganda’s 116 districts utilizing a method based on models using imagery of nighttime lights and agricultural data. Several approaches for estimating grid-level GDP were tested. After comparing model outputs across methods, the Enhanced Light Intensity Model was chosen as the best performing model by analysts at the Frederick S. Pardee Center for International Futures at the University of Denver.
Estimation and Mapping of Sub-National GDP in Uganda Using NPP-VIIRS Imagery: Uganda is one of the poorest nations in the world. It is important to obtain accurate, timely data on socio-economic characteristics sub-nationally, so as to target poverty reduction strategies to those most in need. Many studies have demonstrated that nighttime lights (NTL) can be used to measure human activities. Nevertheless, the methods developed from these studies (1) suffer from coarse resolutions, (2) fail to capture the nonlinearity and multi-scale variability of geospatial data, and (3) perform poorly for agriculture-dependent regions. This study proposes a new enhanced light intensity model (ELIM) to estimate the gross domestic product (GDP) for sub-national units within Uganda. This model is developed by combining the NTL data from the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS), the population data from the Global Human Settlement Layer (GHSL), and information on agricultural production and market prices across several commodity types. This resulted in a gridded dataset for Uganda’s GDP at sub-national levels, to capture the spatial heterogeneity in the economic activity.