Remote sensing-based measurement of Living Environment Deprivation: Improving classical approaches with machine learning

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dc.citation.journalTitlePlos One
dc.contributor.authorArribas-Bel Dspa
dc.contributor.authorPatino JEspa
dc.contributor.authorDuque JCspa
dc.contributor.departmentUniversidad EAFIT. Departamento de Economía y Finanzasspa
dc.contributor.researchgroupResearch in Spatial Economics (RISE)eng
dc.date.accessioned2021-04-12T14:26:18Z
dc.date.available2021-04-12T14:26:18Z
dc.date.issued2017-05-02
dc.description.abstractThis paper provides evidence on the usefulness of very high spatial resolution (VHR) imagery in gathering socioeconomic information in urban settlements. We use land cover, spectral, structure and texture features extracted from a Google Earth image of Liverpool (UK) to evaluate their potential to predict Living Environment Deprivation at a small statistical area level. We also contribute to the methodological literature on the estimation of socioeconomic indices with remote-sensing data by introducing elements from modern machine learning. In addition to classical approaches such as Ordinary Least Squares (OLS) regression and a spatial lag model, we explore the potential of the Gradient Boost Regressor and Random Forests to improve predictive performance and accuracy. In addition to novel predicting methods, we also introduce tools for model interpretation and evaluation such as feature importance and partial dependence plots, or cross-validation. Our results show that Random Forest proved to be the best model with an R2 of around 0.54, followed by Gradient Boost Regressor with 0.5. Both the spatial lag model and the OLS fall behind with significantly lower performances of 0.43 and 0.3, respectively. © 2017 Arribas-Bel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.eng
dc.identifierhttps://eafit.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=6640
dc.identifier.issn19326203
dc.identifier.otherSCOPUS;2-s2.0-85019352713
dc.identifier.urihttp://hdl.handle.net/10784/28053
dc.language.isoengeng
dc.publisherPublic Library of Science
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85018969463&doi=10.1371%2fjournal.pone.0176684&partnerID=40&md5=d2d850a31061064e1a4badd1cbeb3c3b
dc.rightshttps://v2.sherpa.ac.uk/id/publication/issn/1932-6203
dc.sourcePlos One
dc.subject.keywordimageryeng
dc.subject.keywordland useeng
dc.subject.keywordleast square analysiseng
dc.subject.keywordmodeleng
dc.subject.keywordrandom foresteng
dc.subject.keywordremote sensingeng
dc.subject.keywordvalidation processeng
dc.subject.keywordcityeng
dc.subject.keywordEnglandeng
dc.subject.keywordhumaneng
dc.subject.keywordmachine learningeng
dc.subject.keywordprocedureseng
dc.subject.keywordremote sensingeng
dc.subject.keywordsocioeconomicseng
dc.subject.keywordstatistical analysiseng
dc.subject.keywordstatistical modeleng
dc.subject.keywordurban populationeng
dc.subject.keywordCitieseng
dc.subject.keywordData Interpretationeng
dc.subject.keywordStatisticaleng
dc.subject.keywordEnglandeng
dc.subject.keywordHumanseng
dc.subject.keywordLeast-Squares Analysiseng
dc.subject.keywordLinear Modelseng
dc.subject.keywordMachine Learningeng
dc.subject.keywordRemote Sensing Technologyeng
dc.subject.keywordSocioeconomic Factorseng
dc.subject.keywordUrban Populationeng
dc.titleRemote sensing-based measurement of Living Environment Deprivation: Improving classical approaches with machine learningeng
dc.typearticleeng
dc.typeinfo:eu-repo/semantics/articleeng
dc.typeinfo:eu-repo/semantics/publishedVersioneng
dc.typepublishedVersioneng
dc.type.localArtículospa

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