Capturing deprived areas using unsupervised machine learning and open data: a case study in São Paulo, Brazil

Managing the rapid growth of deprived areas (commonly known as slums, informal settlements, etc.) in cities of Low- to Middle-Income Countries (LMICs) demands detailed and consistent information that is often unavailable. Recent Earth Observation (EO) mapping approaches with supervised classification models overlook the diversity of deprived areas and require resource-intensive training sets. In this study, we analyse the potential of unsupervised machine learning (ML) models to capture intra-urban diversity of deprived areas in São Paulo, using solely open geodata. We provide a workflow of characterising deprivation at a city scale with a disaggregated approach, offering scalability and transferability potential. First, we extract a pool of spatial features from open geospatial datasets to characterise the morphological and environmental conditions of the study area. After input preparation, we train and optimise a k-means model, including a coupled feature importance tool. Four cluster types emerged with different deprivation aspects such as higher and lower accessibility to services and infrastructure, sparser and denser occupation; regular and complex morphology; flat and steep terrain. This alternative methodology to capture diversity of deprived areas with open EO-based features can inform locally targeted, thus more efficient, urban policies and interventions.