2021-04-122015-03-010169204618726062WOS;000347508700003SCOPUS;2-s2.0-84919675390http://hdl.handle.net/10784/28020This paper contributes empirical evidence about the usefulness of remote sensing imagery to quantify the degree of poverty at the intra-urban scale. This concept is based on two premises: first, that the physical appearance of an urban settlement is a reflection of the society; and second, that the people who reside in urban areas with similar physical housing conditions have similar social and demographic characteristics. We use a very high spatial resolution (VHR) image from one of the most socioeconomically divergent cities in the world, Medellin (Colombia), to extract information on land cover composition using per-pixel classification and on urban texture and structure using an automated tool for texture and structure feature extraction at object level. We evaluate the potential of these descriptors to explain a measure of poverty known as the Slum Index. We found that these variables explain up to 59% of the variability in the Slum Index. Similar approaches could be used to lower the cost of socioeconomic surveys by developing an econometric model from a sample and applying that model to the rest of the city and to perform intercensal or intersurvey estimates of intra-urban Slum Index maps. (C) 2014 Elsevier B.V. All rights reserved.enghttps://v2.sherpa.ac.uk/id/publication/issn/0169-2046articleIntra-urban povertySlum indexRemote sensingRegional science2021-04-12Duque, Juan C.Patino, Jorge E.Ruiz, Luis A.Pardo-Pascual, Josep E.