Modelling global market access using networks

In this (very much in progress) work we use network science techniques to model access of different locations around the world to the global market. It was shown in the literature that it is possible to surprisingly accurately predict the economic activity in a specific place (measured via the intensity of nightlights) using its geographical features, such as the type of terrain, average temperature, access to waterways, etc.. We attempt to improve the quality of this prediction by incorporating information about features of the surrounding area in a centrality-like fashion. We find that these network features are often more crucial than the local attributes when predicting economic activity.