  Quantifying the improvement in human living standard, as well as the city
growth in developing countries, is a challenging problem due to the lack of
reliable economic data. Therefore, there is a fundamental need for alternate,
largely unsupervised, computational methods that can estimate the economic
conditions in the developing regions. To this end, we propose a new network
science- and representation learning-based approach that can quantify economic
indicators and visualize the growth of various regions. More precisely, we
first create a dynamic network drawn out of high-resolution nightlight
satellite images. We then demonstrate that using representation learning to
mine the resulting network, our proposed approach can accurately predict
spatial gross economic expenditures over large regions. Our method, which
requires only nightlight images and limited survey data, can capture
city-growth, as well as how people's living standard is changing; this can
ultimately facilitate the decision makers' understanding of growth without
heavily relying on expensive and time-consuming surveys.
