Uncovering commercial activity in informal cities
This work addresses the lack of spatial economic data in developing cities with high informality, offering an automated solution for policy-making, though it is incremental as it builds on existing machine learning and imagery techniques.
The researchers tackled the problem of mapping economic activity in informal cities by developing an algorithm to detect 'visible firms' from street view imagery, applied to Medellín, Colombia, revealing a polycentric structure with five clusters and showing that informal activity concentrates in poor, densely populated areas.
Knowledge of the spatial organisation of economic activity within a city is 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 'visible firms' using street view imagery. Using Medellín, Colombia as a case study, we illustrate how this approach can be used to uncover previously unseen economic activity. 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 and registered firms, we find 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.