Uncovering commercial activity in informal cities

TL;DR

This paper presents a machine learning algorithm that detects 'visible firms' in street imagery to uncover informal economic activity in developing cities, revealing spatial patterns and gaps in official data.

Contribution

The study introduces a novel method using street view imagery and machine learning to identify informal economic activity in cities lacking official data.

Findings

Detected polycentric economic structure with five clusters.

Informal activity concentrates in poor, densely populated areas.

Large gap between official data and ground reality.

Abstract

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 Medell\'in, 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.