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dc.contributor.authorAbarca Álvarez, Francisco Javier 
dc.contributor.authorReinoso Bellido, Rafael 
dc.contributor.authorCampos Sánchez, Francisco Sergio 
dc.date.accessioned2020-04-03T08:37:00Z
dc.date.available2020-04-03T08:37:00Z
dc.date.issued2019-12-19
dc.identifier.citationAbarca-Alvarez, F.J.; Reinoso-Bellido, R.; Campos-Sánchez, F.S. Decision Model for Predicting Social Vulnerability Using Artificial Intelligence. ISPRS Int. J. Geo-Inf. 2019, 8, 575. [doi:10.3390/ijgi8120575]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/61026
dc.descriptionThe APC was funded by their authors.es_ES
dc.description.abstractSocial vulnerability, from a socio-environmental point of view, focuses on the identification of disadvantaged or vulnerable groups and the conditions and dynamics of the environments in which they live. To understand this issue, it is important to identify the factors that explain the difficulty of facing situations with a social disadvantage. Due to its complexity and multidimensionality, it is not always easy to point out the social groups and urban areas affected. This research aimed to assess the connection between certain dimensions of social vulnerability and its urban and dwelling context as a fundamental framework in which it occurs using a decision model useful for the planning of social and urban actions. For this purpose, a holistic approximation was carried out on the census and demographic data commonly used in this type of study, proposing the construction of (i) a knowledge model based on Artificial Neural Networks (Self-Organizing Map), with which a demographic profile is identified and characterized whose indicators point to a presence of social vulnerability, and (ii) a predictive model of such a profile based on rules from dwelling variables constructed by conditional inference trees. These models, in combination with Geographic Information Systems, make a decision model feasible for the prediction of social vulnerability based on housing information.es_ES
dc.description.sponsorshipThis research was funded by the University of Granada, grant number PP2016-PIP09es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
dc.subjectSocial Vulnerabilityes_ES
dc.subjectPredictive modelses_ES
dc.subjectUrban modeles_ES
dc.subjectDwellings es_ES
dc.subjectDecision modeles_ES
dc.subjectArtificial neural networkes_ES
dc.subjectSelf-Organizing Mapses_ES
dc.subjectDecision treeses_ES
dc.titleDecision Model for Predicting Social Vulnerability Using Artificial Intelligencees_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.3390/ijgi8120575


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