Uncovering commercial activity in informal cities.

Knowledge of the spatial organization of economic activity within a city is a key to policy concerns. However, in developing cities with high levels of informality, this information is often unavailable. Recent progress in machine learning together with the availability of street imagery offers an affordable and easily automated solution. Here, we propose an algorithm that can detect what we call using street view imagery. By using Medellín, Colombia as a case study, we illustrate how this approach can be used to uncover previously unseen economic activity. By applying spatial analysis to our dataset, we detect a polycentric structure with five distinct clusters located in both the established centre and peripheral areas. Comparing the density of visible establishments with that of registered firms, we infer that informal activity concentrates in poor but densely populated areas. Our findings highlight the large gap between what is captured in official data and the reality on the ground.