Estimating global economic well-being with unlit settlements

Ian McCallum (), Christopher Conrad Maximillian Kyba, Juan Carlos Laso Bayas, Elena Moltchanova, Matt Cooper, Jesus Crespo Cuaresma, Shonali Pachauri, Linda See, Olga Danylo, Inian Moorthy, Myroslava Lesiv, Kimberly Baugh, Christopher D. Elvidge, Martin Hofer and Steffen Fritz
Additional contact information
Ian McCallum: International Institute for Applied Systems Analysis
Christopher Conrad Maximillian Kyba: GFZ German Research Centre for Geosciences, Telegrafenberg
Juan Carlos Laso Bayas: International Institute for Applied Systems Analysis
Elena Moltchanova: University of Canterbury
Matt Cooper: Harvard University
Shonali Pachauri: International Institute for Applied Systems Analysis
Linda See: International Institute for Applied Systems Analysis
Olga Danylo: International Institute for Applied Systems Analysis
Inian Moorthy: International Institute for Applied Systems Analysis
Myroslava Lesiv: International Institute for Applied Systems Analysis
Kimberly Baugh: University of Colorado, 216 UCB
Christopher D. Elvidge: Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines
Martin Hofer: International Institute for Applied Systems Analysis
Steffen Fritz: International Institute for Applied Systems Analysis

Nature Communications, 2022, vol. 13, issue 1, 1-8

Abstract: Abstract It is well established that nighttime radiance, measured from satellites, correlates with economic prosperity across the globe. In developing countries, areas with low levels of detected radiance generally indicate limited development – with unlit areas typically being disregarded. Here we combine satellite nighttime lights and the world settlement footprint for the year 2015 to show that 19% of the total settlement footprint of the planet had no detectable artificial radiance associated with it. The majority of unlit settlement footprints are found in Africa (39%), rising to 65% if we consider only rural settlement areas, along with numerous countries in the Middle East and Asia. Significant areas of unlit settlements are also located in some developed countries. For 49 countries spread across Africa, Asia and the Americas we are able to predict and map the wealth class obtained from ~2,400,000 geo-located households based upon the percent of unlit settlements, with an overall accuracy of 87%.

Date: 2022
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DOI: 10.1038/s41467-022-30099-9

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