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Quantifying the improvement in human living standard, as well as the citygrowth in developing countries, is a challenging problem due to the lack ofreliable economic data. Therefore, there is a fundamental need for alternate,largely unsupervised, computational methods that can estimate the economicconditions in the developing regions. To this end, we propose a new networkscience- and representation learning-based approach that can quantify economicindicators and visualize the growth of various regions. More precisely, wefirst create a dynamic network drawn out of high-resolution nightlightsatellite images. We then demonstrate that using representation learning tomine the resulting network, our proposed approach can accurately predictspatial gross economic expenditures over large regions. Our method, whichrequires only nightlight images and limited survey data, can capturecity-growth, as well as how people's living standard is changing; this canultimately facilitate the decision makers' understanding of growth withoutheavily relying on expensive and time-consuming surveys.

A Dynamic Network and Representation LearningApproach for Quantifying Economic Growth fromSatellite Imagery