Delimitation of metropolitan centralities in Latin America: a computational approach applied to Buenos Aires

Abstract

Identifying and delineating urban centralities is fundamental to understanding the dynamics and inequalities of Latin American metropolises, a challenge that has historically been significant in urban studies. This article leverages the tools of the computational shift in the social sciences to address this problem in the region, formulating a mixed methodological strategy that prioritizes cost, parsimony, and reproducibility. Big data from Open Street Maps, the DBSCAN clustering technique, Python programming, fieldwork, and photographic documentation were used for the challenging case of Buenos Aires. As a result, the city's centralities were identified, illustrating the diversity of centers across different types and hierarchies. Finally, the article reflects on the lessons learned from the case study to replicate this experience in other cities in the region.