Analysis of PCA with georeferenced data. An application in tourism industry

Autores
Luna, Laura Isabel
Año de publicación
2019
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Fil: Luna, Laura Isabel. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.
The spatial analysis of the tourism characteristic activities allows us to generate information about the structure of tourism industry, which is necessary for decision making. In this work, toursim characteristic activities in the departments of Córdoba were mapped. The methodological innovation lies in the generation of statistics for multidimensional spatial data. Multivariate methods with and without spatial restrictions were studied and compared in their performance in the application context. The comparison showed that the spatial principal components analysis (MULTISPATI-PCA) yielded a higher degree of spatial structuring of the components that summarize tourism activities than principal components analysis (PCA). The maps of the summarized variables showed a higher level of structure with MULTISPATI-PCA. Departments were classified according to the participation of tourism activities in the value added of tourism using the spatial principal components obtained as input of the cluster fuzzy k-means analysis. Finally, a mapping was performed based on the participation of the tourism value added in the gross regional product of the different departments and the variations in the participation of the different activities that make up the aggregate was analyzed for the period 2001-2014.
Fil: Luna, Laura Isabel. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.
Economía, Econometría
Materia
Industry tourism
Spatial multivariate annalysis
Fuzzy k-means
Nivel de accesibilidad
acceso abierto
Condiciones de uso
Repositorio
Repositorio Digital Universitario (UNC)
Institución
Universidad Nacional de Córdoba
OAI Identificador
oai:rdu.unc.edu.ar:11086/549688
Acceder
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oai_identifier_str oai:rdu.unc.edu.ar:11086/549688
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repository_id_str 2572
network_name_str Repositorio Digital Universitario (UNC)
spelling Analysis of PCA with georeferenced data. An application in tourism industryLuna, Laura IsabelIndustry tourismSpatial multivariate annalysisFuzzy k-meansFil: Luna, Laura Isabel. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.The spatial analysis of the tourism characteristic activities allows us to generate information about the structure of tourism industry, which is necessary for decision making. In this work, toursim characteristic activities in the departments of Córdoba were mapped. The methodological innovation lies in the generation of statistics for multidimensional spatial data. Multivariate methods with and without spatial restrictions were studied and compared in their performance in the application context. The comparison showed that the spatial principal components analysis (MULTISPATI-PCA) yielded a higher degree of spatial structuring of the components that summarize tourism activities than principal components analysis (PCA). The maps of the summarized variables showed a higher level of structure with MULTISPATI-PCA. Departments were classified according to the participation of tourism activities in the value added of tourism using the spatial principal components obtained as input of the cluster fuzzy k-means analysis. Finally, a mapping was performed based on the participation of the tourism value added in the gross regional product of the different departments and the variations in the participation of the different activities that make up the aggregate was analyzed for the period 2001-2014.Fil: Luna, Laura Isabel. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.Economía, Econometría2019-09info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf978-950-34-1854-3http://hdl.handle.net/11086/549688enginfo:eu-repo/semantics/openAccessreponame:Repositorio Digital Universitario (UNC)instname:Universidad Nacional de Córdobainstacron:UNC2025-09-29T13:43:12Zoai:rdu.unc.edu.ar:11086/549688Institucionalhttps://rdu.unc.edu.ar/Universidad públicaNo correspondehttp://rdu.unc.edu.ar/oai/snrdoca.unc@gmail.comArgentinaNo correspondeNo correspondeNo correspondeopendoar:25722025-09-29 13:43:12.316Repositorio Digital Universitario (UNC) - Universidad Nacional de Córdobafalse
dc.title.none.fl_str_mv Analysis of PCA with georeferenced data. An application in tourism industry
title Analysis of PCA with georeferenced data. An application in tourism industry
spellingShingle Analysis of PCA with georeferenced data. An application in tourism industry
Luna, Laura Isabel
Industry tourism
Spatial multivariate annalysis
Fuzzy k-means
title_short Analysis of PCA with georeferenced data. An application in tourism industry
title_full Analysis of PCA with georeferenced data. An application in tourism industry
title_fullStr Analysis of PCA with georeferenced data. An application in tourism industry
title_full_unstemmed Analysis of PCA with georeferenced data. An application in tourism industry
title_sort Analysis of PCA with georeferenced data. An application in tourism industry
dc.creator.none.fl_str_mv Luna, Laura Isabel
author Luna, Laura Isabel
author_facet Luna, Laura Isabel
author_role author
dc.subject.none.fl_str_mv Industry tourism
Spatial multivariate annalysis
Fuzzy k-means
topic Industry tourism
Spatial multivariate annalysis
Fuzzy k-means
dc.description.none.fl_txt_mv Fil: Luna, Laura Isabel. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.
The spatial analysis of the tourism characteristic activities allows us to generate information about the structure of tourism industry, which is necessary for decision making. In this work, toursim characteristic activities in the departments of Córdoba were mapped. The methodological innovation lies in the generation of statistics for multidimensional spatial data. Multivariate methods with and without spatial restrictions were studied and compared in their performance in the application context. The comparison showed that the spatial principal components analysis (MULTISPATI-PCA) yielded a higher degree of spatial structuring of the components that summarize tourism activities than principal components analysis (PCA). The maps of the summarized variables showed a higher level of structure with MULTISPATI-PCA. Departments were classified according to the participation of tourism activities in the value added of tourism using the spatial principal components obtained as input of the cluster fuzzy k-means analysis. Finally, a mapping was performed based on the participation of the tourism value added in the gross regional product of the different departments and the variations in the participation of the different activities that make up the aggregate was analyzed for the period 2001-2014.
Fil: Luna, Laura Isabel. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.
Economía, Econometría
description Fil: Luna, Laura Isabel. Universidad Nacional de Córdoba. Facultad de Ciencias Económicas; Argentina.
publishDate 2019
dc.date.none.fl_str_mv 2019-09
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv 978-950-34-1854-3
http://hdl.handle.net/11086/549688
identifier_str_mv 978-950-34-1854-3
url http://hdl.handle.net/11086/549688
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositorio Digital Universitario (UNC)
instname:Universidad Nacional de Córdoba
instacron:UNC
reponame_str Repositorio Digital Universitario (UNC)
collection Repositorio Digital Universitario (UNC)
instname_str Universidad Nacional de Córdoba
instacron_str UNC
institution UNC
repository.name.fl_str_mv Repositorio Digital Universitario (UNC) - Universidad Nacional de Córdoba
repository.mail.fl_str_mv oca.unc@gmail.com
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