Quantifying slumness with remote sensing data

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dc.contributor.authorDuque, Juan C.
dc.contributor.authorPatino, Jorge E.
dc.contributor.authorRuiz, Luis A.
dc.contributor.authorPardo-Pascual, Josep E.
dc.coverage.spatialMedellín de: Lat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degrees Long: 075 36 00 W degrees minutes Long: -75.6000 decimal degreeseng
dc.creator.emailjduquec1@eafit.edu.cospa
dc.date.accessioned2013-08-08T18:05:59Z
dc.date.available2013-08-08T18:05:59Z
dc.date.issued2013-08-08
dc.description.abstractThe presence of slums in a city is an indicator of poverty and its proper delimitation is a matter of interest for researchers and policy makers. Socio-economic data from surveys and censuses are the primary source of information to identify and quantify slumness within a city or a town. One problem of using survey data for quantifying slumness is that this type of data is usually collected every ten years and is an expensive and time consuming process. Based on the premise that the physical appearance of an urban settlement is a reflection of the society that created it and on the assumption that people living in urban areas with similar physical housing conditions will have similar social and demographic characteristics (Jain, 2008; Taubenb¨ock et al., 2009b); this paper uses data from Medellin City, Colombia, to estimate slum index using solely remote sensing data from an orthorectified, pan-sharpened, natural color Quickbird scene. For Medellin city, the percentage of clay roofs cover and the mean swimming pool density at the analytical region level can explain up to 59% of the variability in the slum index. Structure and texture measures are useful to characterize the differences in the homogeneity of the spatial pattern of the urban layout and they improve the explanatory power of the statistical models when taken into account. When no other information is used, they can explain up to 30% of the variability of the slum index. The results of this research are encouraging and many researchers, urban planners and policy makers could benefit from this rapid and low cost approach to characterize the intra-urban variations of slumness in cities with sparse data or no data at all.eng
dc.identifier.jelC8
dc.identifier.jelR14
dc.identifier.urihttp://hdl.handle.net/10784/1050
dc.language.isoengeng
dc.publisherUniversidad EAFITspa
dc.publisher.departmentEscuela de Economía y Finanzasspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccesseng
dc.rights.localAcceso abiertospa
dc.subject.keywordRegional Sciencespa
dc.subject.keywordRemote Sensingspa
dc.subject.keywordSlumspa
dc.subject.keywordGEOBIAspa
dc.titleQuantifying slumness with remote sensing dataeng
dc.typeworkingPapereng
dc.typeinfo:eu-repo/semantics/workingPapereng
dc.type.hasVersiondrafteng
dc.type.localDocumento de trabajo de investigaciónspa

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