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Estimating quality of life dimensions from urban spatial pattern metrics

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Estimating quality of life dimensions from urban spatial pattern metrics

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dc.contributor.author Sapena, Marta es_ES
dc.contributor.author Wurm, Michael es_ES
dc.contributor.author Taubenböck, Hannes es_ES
dc.contributor.author Tuia, Devis es_ES
dc.contributor.author Ruiz Fernández, Luis Ángel es_ES
dc.date.accessioned 2022-07-08T18:05:13Z
dc.date.available 2022-07-08T18:05:13Z
dc.date.issued 2021-01 es_ES
dc.identifier.issn 0198-9715 es_ES
dc.identifier.uri http://hdl.handle.net/10251/183992
dc.description.abstract [EN] The spatial structure of urban areas plays a major role in the daily life of dwellers. The current policy framework to ensure the quality of life of inhabitants leaving no one behind, leads decision-makers to seek better-informed choices for the sustainable planning of urban areas. Thus, a better understanding between the spatial structure of cities and their socio-economic level is of crucial relevance. Accordingly, the purpose of this paper is to quantify this two-way relationship. Therefore, we measured spatial patterns of 31 cities in North Rhine-Westphalia, Germany. We rely on spatial pattern metrics derived from a Local Climate Zone classification obtained by fusing remote sensing and open GIS data with a machine learning approach. Based upon the data, we quantified the relationship between spatial pattern metrics and socio-economic variables related to `education¿, `health¿, `living conditions¿, `labor¿, and `transport¿ by means of multiple linear regression models, explaining the variability of the socio-economic variables from 43% up to 82%. Additionally, we grouped cities according to their level of `quality of life¿ using the socio-economic variables, and found that the spatial pattern of low-dense built-up types was different among socio-economic groups. The proposed methodology described in this paper is transferable to other datasets, levels, and regions. This is of great potential, due to the growing availability of open statistical and satellite data and derived products. Moreover, we discuss the limitations and needed considerations when conducting such studies. es_ES
dc.description.sponsorship This research has been partially funded by the Spanish Ministerio de Economia y Competitividad and European Regional Development Fund (CGL2016-80705-R) and by the Swiss National Science Foundation (PP00P2-150593). es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Computers Environment and Urban Systems es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Spatial metrics es_ES
dc.subject Socio-economic variables es_ES
dc.subject Local climate zones es_ES
dc.subject Quality of life es_ES
dc.subject Remote sensing es_ES
dc.subject.classification INGENIERIA CARTOGRAFICA, GEODESIA Y FOTOGRAMETRIA es_ES
dc.title Estimating quality of life dimensions from urban spatial pattern metrics es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.compenvurbsys.2020.101549 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/SNSF//PP00P2-150593/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI//CGL2016-80705-R//ANALISIS Y VALIDACION DE PARAMETROS DE ESTRUCTURA FORESTAL DERIVADOS DE LIDAR Y OTRAS TECNICAS EMERGENTES Y SU INCIDENCIA EN LA MODELIZACION DEL POTENCIAL COMBUSTIBLE/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Cartográfica Geodesia y Fotogrametría - Departament d'Enginyeria Cartogràfica, Geodèsia i Fotogrametria es_ES
dc.description.bibliographicCitation Sapena, M.; Wurm, M.; Taubenböck, H.; Tuia, D.; Ruiz Fernández, LÁ. (2021). Estimating quality of life dimensions from urban spatial pattern metrics. Computers Environment and Urban Systems. 85:1-11. https://doi.org/10.1016/j.compenvurbsys.2020.101549 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.compenvurbsys.2020.101549 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 11 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 85 es_ES
dc.relation.pasarela S\418803 es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder Swiss National Science Foundation es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.subject.ods 11.- Conseguir que las ciudades y los asentamientos humanos sean inclusivos, seguros, resilientes y sostenibles es_ES


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