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dc.contributor.authorAbarca Álvarez, Francisco Javier 
dc.contributor.authorCampos Sánchez, Francisco Sergio 
dc.contributor.authorOsuna Pérez, Fernando 
dc.date.accessioned2020-02-10T12:36:38Z
dc.date.available2020-02-10T12:36:38Z
dc.date.issued2019-11-23
dc.identifier.citationAbarca-Alvarez, F. J., Campos-Sánchez, F. S., & Osuna-Pérez, F. (2019). Urban Shape and Built Density Metrics through the Analysis of European Urban Fabrics Using Artificial Intelligence. Sustainability, 11(23), 6622.es_ES
dc.identifier.urihttp://hdl.handle.net/10481/59549
dc.description.abstractIn recent decades, the concept of urban density has been considered key to the creation of sustainable urban fabrics. However, when it comes to measuring the built density, a difficulty has been observed in defining valid measurement indicators universally. With the intention of identifying the variables that allow the best characterization of the shape of urban fabrics and of obtaining the metrics of their density, a multi-variable analysis methodology from the field of artificial intelligence is proposed. The main objective of this paper was to evaluate the capacity and interest of such a methodology from standard indicators of the built density, measured at various urban scales, (i) to cluster differentiated urban profiles in a robust way by assessing the results statistically, and (ii) to obtain the metrics that characterize them with an identity. As a case study, this methodology was applied to the state of the art European urban fabrics (N = 117) by simultaneously integrating 13 regular parameters to qualify urban shape and density. It was verified that the profiles obtained were more robust than those based on a limited number of indicators, evidencing that the proposed methodology offers operational opportunities in urban management by allowing the comparison of a fabric with the identified profiles.es_ES
dc.description.sponsorshipThis research was funded by the University of Granada, grant number PP2016-PIP09 and their authors.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectUrban shapees_ES
dc.subjectBuilt densityes_ES
dc.subjectUrban fabrices_ES
dc.subjectArtificial neural networkes_ES
dc.subjectSelf-Organizing Mapses_ES
dc.titleUrban Shape and Built Density Metrics through the Analysis of European Urban Fabrics Using Artificial Intelligencees_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.3390/su11236622


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Atribución 3.0 España
Except where otherwise noted, this item's license is described as Atribución 3.0 España