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Analysis of Tourist Systems Predictive Models Applied to Growing Sun and Beach Tourist Destination

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  • Miguel Angel Ruiz Palacios

    (Departament of Business, North Lima Campus, Universidad César Vallejo, Lima 15314, Peru
    Faculty of Business Studies, Universidad Privada del Norte, Lima 15314, Peru)

  • Cristiana Pereira Texeira de Oliveira

    (General Director and Rector, Universidad Europea de Canarias, 38300 Tenerife, Spain)

  • José Serrano González

    (Departament of Social Sciences, Universidad Europea de Canarias, 38300 Tenerife, Spain)

  • Soledad Saénz Flores

    (School of Administration in Tourism and Hospitality, North Lima Campus, Universidad César Vallejo, Lima 15314, Peru)

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 attractions and local resources including reserved zone Lomas de Ancón, with 10,962 hectares. It used a qualitative method and its design is grounded theory and phenomenological. The research covers the period from May 2018 to March 2019, where it was possible to appreciate the high tourist demand and wild flora and fauna of the Lomas de Ancón in its two seasons: winter season (2018) and summer 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.

Suggested Citation

  • Miguel Angel Ruiz Palacios & Cristiana Pereira Texeira de Oliveira & José Serrano González & Soledad Saénz Flores, 2021. "Analysis of Tourist Systems Predictive Models Applied to Growing Sun and Beach Tourist Destination," Sustainability, MDPI, vol. 13(2), pages 1-24, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:2:p:785-:d:480634
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    References listed on IDEAS

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    2. Jessie Bravo & Roger Alarcón & Carlos Valdivia & Oscar Serquén, 2023. "Application of Machine Learning Techniques to Predict Visitors to the Tourist Attractions of the Moche Route in Peru," Sustainability, MDPI, vol. 15(11), pages 1-25, June.
    3. Juan Diego López-Arquillo & Cristiana Oliveira & Jose Serrano González & Amador Durán Sánchez, 2023. "Interdependence in Coastal Tourist Territories between Marine Litter and Immediate Tourist Zoning Density: Methodological Approach for Urban Sustainable Development," Land, MDPI, vol. 13(1), pages 1-24, December.

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