We study how the improvement of highway networks between China and neighboring countries in Central Asia has affected economic development in the region. By applying deep learning to remotely sensed data with a 5m spatial resolution, we create digital maps of enlarging road networks and expanding built-up footprints between 2009 and 2019 for a region that covers 1.3 million sq. km. Using a gravity-based spatial economic framework, we estimate that a one percent increase in market access due to expanded road networks leads to one percent increase in built-up land area for micro-spatial market areas. We further evaluate heterogeneity in impacts on locations in China versus locations in Central Asian nations and across markets according to initial levels of economic development. Our analysis provides a framework to evaluate China’s Belt-Road Initiative using remotely sensed data alone.

The paper studies the evolution of settlements in sub-Saharan Africa from 1975 until 2014, a period in which total built area increased by 244%. Our empirical work is based on satellite imaging from which we identify built areas as small as 900 sq meters. Our focus is on the geography of settlements and patterns of competition and complementarity between settlements of different sizes and types. We develop a theoretical model of an urban hierarchy on which to base econometric specifications. Our main results are based on classifying settlements into three size classes, the exact specification of which is country-specific. As predicted by the model, settlements generally grow more slowly if close to neighbors experiencing positive growth in the same size class, and grow somewhat faster if close to settlements given positive shocks in one of the other size classes. This produces an outcome in which settlements, particularly those in the two larger size classes, tend to be more evenly spaced than would be predicted by chance, as is predicted by the theory.