Preprint Article Version 1 This version is not peer-reviewed

Estimation and Mapping of Sub-National GDP in Uganda Using NPP-VIIRS Imagery

Version 1 : Received: 20 November 2018 / Approved: 21 November 2018 / Online: 21 November 2018 (07:13:10 CET)

A peer-reviewed article of this Preprint also exists.

Wang, X.; Rafa, M.; Moyer, J.D.; Li, J.; Scheer, J.; Sutton, P. Estimation and Mapping of Sub-National GDP in Uganda Using NPP-VIIRS Imagery. Remote Sens. 2019, 11, 163. Wang, X.; Rafa, M.; Moyer, J.D.; Li, J.; Scheer, J.; Sutton, P. Estimation and Mapping of Sub-National GDP in Uganda Using NPP-VIIRS Imagery. Remote Sens. 2019, 11, 163.

Journal reference: Remote Sens. 2019, 11, 163
DOI: 10.3390/rs11020163

Abstract

Uganda is one of the poorest nations in the world. To address the developmental challenges and understand social and economic status, it is important to obtain accurate data in a timely manner. Many studies have demonstrated that nighttime lights (NTL) can be used to measure human activities. Nevertheless, 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) at sub-national scales for 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 GDP and GDP per capita for Uganda at 1 km spatial resolution and district level to capture the spatial heterogeneity in economic activity.

Subject Areas

GDP; nighttime lights; agriculture; development

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