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

dc.contributor.authorRuiz Palacios, Miguel Angel
dc.contributor.authorPereira Teixeira de Oliveira, Cristiana
dc.contributor.authorSerrano González, José
dc.contributor.authorSaenz Flores, Soledad Gisela
dc.date.accessioned2021-11-16T21:58:52Z
dc.date.available2021-11-16T21:58:52Z
dc.date.issued2021-01-15
dc.identifier.citationRuiz, 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/su13020785es_PE
dc.identifier.urihttps://hdl.handle.net/11537/28432
dc.description.abstractABSTRACT 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.es_PE
dc.formatapplication/pdfes_PE
dc.language.isoenges_PE
dc.publisherMDPIes_PE
dc.rightsinfo:eu-repo/semantics/openAccesses_PE
dc.rightsAtribución-NoComercial-CompartirIgual 3.0 Estados Unidos de América*
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/3.0/us/*
dc.sourceUniversidad Privada del Nortees_PE
dc.sourceRepositorio Institucional - UPNes_PE
dc.subjectTurismoes_PE
dc.subjectActividad turísticaes_PE
dc.subjectDemanda turísticaes_PE
dc.subjectRecursos naturaleses_PE
dc.subjectPronósticoes_PE
dc.titleAnalysis of tourist systems predictive models applied to growing sun and beach tourist destinationes_PE
dc.typeinfo:eu-repo/semantics/bachelorThesises_PE
dc.publisher.countryCHes_PE
dc.identifier.journalSustainabilityes_PE
dc.description.peer-reviewRevisión por pareses_PE
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#5.02.04es_PE
dc.description.sedeLos Olivoses_PE
dc.identifier.doihttps://doi.org/10.3390/su13020785


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