Analysis of tourist systems predictive models applied to growing sun and beach tourist destination

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ABSTRACT This study aims to present a new diagnosis model of Sun and beach destinations, we analyzed a set of explanatory theories about the tourism system, because current models do not reflect the real dynamics of an emerging tourist destination. We create a new predictive model so it served us to...

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Detalles Bibliográficos
Autores: Ruiz Palacios, Miguel Angel, Pereira Teixeira de Oliveira, Cristiana, Serrano González, José, Saenz Flores, Soledad Gisela
Formato: tesis de grado
Fecha de Publicación:2021
Institución:Universidad Privada del Norte
Repositorio:UPN-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.upn.edu.pe:11537/28432
Enlace del recurso:https://hdl.handle.net/11537/28432
https://doi.org/10.3390/su13020785
Nivel de acceso:acceso abierto
Materia:Turismo
Actividad turística
Demanda turística
Recursos naturales
Pronóstico
https://purl.org/pe-repo/ocde/ford#5.02.04
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dc.title.es_PE.fl_str_mv Analysis of tourist systems predictive models applied to growing sun and beach tourist destination
title Analysis of tourist systems predictive models applied to growing sun and beach tourist destination
spellingShingle Analysis of tourist systems predictive models applied to growing sun and beach tourist destination
Ruiz Palacios, Miguel Angel
Turismo
Actividad turística
Demanda turística
Recursos naturales
Pronóstico
https://purl.org/pe-repo/ocde/ford#5.02.04
title_short Analysis of tourist systems predictive models applied to growing sun and beach tourist destination
title_full Analysis of tourist systems predictive models applied to growing sun and beach tourist destination
title_fullStr Analysis of tourist systems predictive models applied to growing sun and beach tourist destination
title_full_unstemmed Analysis of tourist systems predictive models applied to growing sun and beach tourist destination
title_sort Analysis of tourist systems predictive models applied to growing sun and beach tourist destination
author Ruiz Palacios, Miguel Angel
author_facet Ruiz Palacios, Miguel Angel
Pereira Teixeira de Oliveira, Cristiana
Serrano González, José
Saenz Flores, Soledad Gisela
author_role author
author2 Pereira Teixeira de Oliveira, Cristiana
Serrano González, José
Saenz Flores, Soledad Gisela
author2_role author
author
author
dc.contributor.author.fl_str_mv Ruiz Palacios, Miguel Angel
Pereira Teixeira de Oliveira, Cristiana
Serrano González, José
Saenz Flores, Soledad Gisela
dc.subject.es_PE.fl_str_mv Turismo
Actividad turística
Demanda turística
Recursos naturales
Pronóstico
topic Turismo
Actividad turística
Demanda turística
Recursos naturales
Pronóstico
https://purl.org/pe-repo/ocde/ford#5.02.04
dc.subject.ocde.es_PE.fl_str_mv https://purl.org/pe-repo/ocde/ford#5.02.04
description ABSTRACT This study aims to present a new diagnosis model of Sun and beach destinations, we analyzed a set of explanatory theories about the tourism system, because current models do not reflect the real dynamics of an emerging tourist destination. We create a new predictive model so it served us to be used as a diagnostic method for the tourism system. Ancon district is a coastal town of Peru, it is the second-largest and oldest of Metropolitan Lima district. The study analyzed all tourist attractionsandlocalresourcesincludingreservedzoneLomasdeAncón,with10,962hectares. Itused a qualitative method and its design is grounded theory and phenomenological. The research covers theperiodfromMay2018toMarch2019,whereitwaspossibletoappreciatethehightouristdemand andwildfloraandfaunaoftheLomasdeAncóninitstwoseasons: winterseason(2018)andsummer 2019 (dry season). The study concludes that the new analysis model allows us identifying and understanding the dynamic and potential of sun and beach tourist destinations in the growth phase. The Ancón district has resources and attractions that would allow it to develop new tourist products and diversify the local tourist offer.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-11-16T21:58:52Z
dc.date.available.none.fl_str_mv 2021-11-16T21:58:52Z
dc.date.issued.fl_str_mv 2021-01-15
dc.type.es_PE.fl_str_mv info:eu-repo/semantics/bachelorThesis
format bachelorThesis
dc.identifier.citation.es_PE.fl_str_mv Ruiz, M. A., ...[et al.]. (2021). Analysis of tourist systems predictive models applied to growing sun and beach tourist destination. Sustainability, 13 (2). https://doi.org/10.3390/su13020785
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/11537/28432
dc.identifier.journal.es_PE.fl_str_mv Sustainability
dc.identifier.doi.none.fl_str_mv https://doi.org/10.3390/su13020785
identifier_str_mv Ruiz, M. A., ...[et al.]. (2021). Analysis of tourist systems predictive models applied to growing sun and beach tourist destination. Sustainability, 13 (2). https://doi.org/10.3390/su13020785
Sustainability
url https://hdl.handle.net/11537/28432
https://doi.org/10.3390/su13020785
dc.language.iso.es_PE.fl_str_mv eng
language eng
dc.relation.ispartof.fl_str_mv SUNEDU
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eu_rights_str_mv openAccess
rights_invalid_str_mv Atribución-NoComercial-CompartirIgual 3.0 Estados Unidos de América
https://creativecommons.org/licenses/by-nc-sa/3.0/us/
dc.format.es_PE.fl_str_mv application/pdf
dc.publisher.es_PE.fl_str_mv MDPI
dc.publisher.country.es_PE.fl_str_mv CH
dc.source.es_PE.fl_str_mv Universidad Privada del Norte
Repositorio Institucional - UPN
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instname:Universidad Privada del Norte
instacron:UPN
instname_str Universidad Privada del Norte
instacron_str UPN
institution UPN
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collection UPN-Institucional
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spelling Ruiz Palacios, Miguel AngelPereira Teixeira de Oliveira, CristianaSerrano González, JoséSaenz Flores, Soledad Gisela2021-11-16T21:58:52Z2021-11-16T21:58:52Z2021-01-15Ruiz, M. A., ...[et al.]. (2021). Analysis of tourist systems predictive models applied to growing sun and beach tourist destination. Sustainability, 13 (2). https://doi.org/10.3390/su13020785https://hdl.handle.net/11537/28432Sustainabilityhttps://doi.org/10.3390/su13020785ABSTRACT This study aims to present a new diagnosis model of Sun and beach destinations, we analyzed a set of explanatory theories about the tourism system, because current models do not reflect the real dynamics of an emerging tourist destination. We create a new predictive model so it served us to be used as a diagnostic method for the tourism system. Ancon district is a coastal town of Peru, it is the second-largest and oldest of Metropolitan Lima district. 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Analysis of tourist systems predictive models applied to growing sun and beach tourist destination
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