Predictive Modelling for Sustainable Pilgrim Flow Management on the Camino de Santiago

When destinations are in a growth or maturity phase, two simultaneous debates usually arise: is there overtourism? and ‐if it exists‐ does it have negative consequences? The literature has been concerned with providing scientific answers to these questions analysing cases of urban and sun and sand d...

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Autores: Atrio Lema, Yago, Neira Gómez, Isabel, Río, María L. del
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Universidad de La Laguna (ULL)
Repositorio:RIULL. Repositorio Institucional de la Universidad de La Laguna
OAI Identifier:oai:riull.ull.es:915/41979
Acceso en línea:http://riull.ull.es/xmlui/handle/915/41979
Access Level:acceso abierto
Palabra clave:Overtourism
Predictive models
Management
El Camino de Santiago
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spelling Predictive Modelling for Sustainable Pilgrim Flow Management on the Camino de SantiagoModelización predictiva para la gestión sostenible del caudal peregrino en el Camino de SantiagoAtrio Lema, YagoNeira Gómez, IsabelRío, María L. delOvertourismPredictive modelsManagementEl Camino de SantiagoWhen destinations are in a growth or maturity phase, two simultaneous debates usually arise: is there overtourism? and ‐if it exists‐ does it have negative consequences? The literature has been concerned with providing scientific answers to these questions analysing cases of urban and sun and sand destinations. The differential elements of rural destinations in relation to this topic have usually been neglected. This study presents a prediction instrument built specifically for a growing destination located ‐ almost entirely ‐ in a rural environment: El Camino de Santiago. Based on the information collected over the last 20 years by the Pilgrim’s Welcome Office receiving more than 4 million pilgrims, this instrument is aimed at predicting the number of pilgrims who will pass through a series of hotspots ‐employing Seasonal Autoregressive Integrated Moving Average (SARIMA), and Trigonometric seasonality, Box‐Cox transformation, ARMA errors, Trend and Seasonal Components (TBATS) models‐ to help control management of pilgrim flows and thus counteract the possible negative consequences of overtourism, optimising the experience for tourists, business owners, and residents of the hotspots.Cuando los destinos se encuentran en fase de crecimiento o madurez, suelen surgir dos debates simultáneos: ¿existe sobreturismo? y ‐en caso de que exista‐ ¿tiene consecuencias negativas? La literatura se ha ocupado de dar respuestas científicas a estas preguntas analizando casos de destinos urbanos y de sol y playa. Los elementos diferenciales de los destinos rurales en relación con este tema han sido habitualmente desatendidos. Este estudio presenta un instrumento de predicción construido específicamente para un des‐ tino en crecimiento ubicado ‐casi en su totalidad‐ en un entorno rural: Camino de Santiago. A partir de la información recogida en los últimos 20 años por la Oficina de Acogida al Peregrino sobre más de 4 millones de peregrinos, este instrumento de predicción tiene como objetivo predecir el número de peregrinos que pasarán por una serie de hotspots ‐empleando la Media Móvil Autorregresiva Estacional Integrada (SARIMA)‐, y Trigonometric seasonality, Box‐Cox transformation, ARMA errors, Trend and Seasonal Components (TBATS) models‐ ayudando a la gestión del flujo de peregrinos y controlando así las posibles consecuencias negativas del sobreturismo, optimizando la experiencia de turistas, empresarios y residentes de los hotspots.Instituto Universitario de Ciencias Políticas y Sociales de la Universidad de La Laguna202520252025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://riull.ull.es/xmlui/handle/915/41979reponame:RIULL. Repositorio Institucional de la Universidad de La Lagunainstname:Universidad de La Laguna (ULL)InglésPasos, Año 2025, vol. 23, n. 2, pp.433-448;Attribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riull.ull.es:915/419792026-06-22T13:13:57Z
dc.title.none.fl_str_mv Predictive Modelling for Sustainable Pilgrim Flow Management on the Camino de Santiago
Modelización predictiva para la gestión sostenible del caudal peregrino en el Camino de Santiago
title Predictive Modelling for Sustainable Pilgrim Flow Management on the Camino de Santiago
spellingShingle Predictive Modelling for Sustainable Pilgrim Flow Management on the Camino de Santiago
Atrio Lema, Yago
Overtourism
Predictive models
Management
El Camino de Santiago
title_short Predictive Modelling for Sustainable Pilgrim Flow Management on the Camino de Santiago
title_full Predictive Modelling for Sustainable Pilgrim Flow Management on the Camino de Santiago
title_fullStr Predictive Modelling for Sustainable Pilgrim Flow Management on the Camino de Santiago
title_full_unstemmed Predictive Modelling for Sustainable Pilgrim Flow Management on the Camino de Santiago
title_sort Predictive Modelling for Sustainable Pilgrim Flow Management on the Camino de Santiago
dc.creator.none.fl_str_mv Atrio Lema, Yago
Neira Gómez, Isabel
Río, María L. del
author Atrio Lema, Yago
author_facet Atrio Lema, Yago
Neira Gómez, Isabel
Río, María L. del
author_role author
author2 Neira Gómez, Isabel
Río, María L. del
author2_role author
author
dc.subject.none.fl_str_mv Overtourism
Predictive models
Management
El Camino de Santiago
topic Overtourism
Predictive models
Management
El Camino de Santiago
description When destinations are in a growth or maturity phase, two simultaneous debates usually arise: is there overtourism? and ‐if it exists‐ does it have negative consequences? The literature has been concerned with providing scientific answers to these questions analysing cases of urban and sun and sand destinations. The differential elements of rural destinations in relation to this topic have usually been neglected. This study presents a prediction instrument built specifically for a growing destination located ‐ almost entirely ‐ in a rural environment: El Camino de Santiago. Based on the information collected over the last 20 years by the Pilgrim’s Welcome Office receiving more than 4 million pilgrims, this instrument is aimed at predicting the number of pilgrims who will pass through a series of hotspots ‐employing Seasonal Autoregressive Integrated Moving Average (SARIMA), and Trigonometric seasonality, Box‐Cox transformation, ARMA errors, Trend and Seasonal Components (TBATS) models‐ to help control management of pilgrim flows and thus counteract the possible negative consequences of overtourism, optimising the experience for tourists, business owners, and residents of the hotspots.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://riull.ull.es/xmlui/handle/915/41979
url http://riull.ull.es/xmlui/handle/915/41979
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Pasos, Año 2025, vol. 23, n. 2, pp.433-448;
dc.rights.none.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Instituto Universitario de Ciencias Políticas y Sociales de la Universidad de La Laguna
publisher.none.fl_str_mv Instituto Universitario de Ciencias Políticas y Sociales de la Universidad de La Laguna
dc.source.none.fl_str_mv reponame:RIULL. Repositorio Institucional de la Universidad de La Laguna
instname:Universidad de La Laguna (ULL)
instname_str Universidad de La Laguna (ULL)
reponame_str RIULL. Repositorio Institucional de la Universidad de La Laguna
collection RIULL. Repositorio Institucional de la Universidad de La Laguna
repository.name.fl_str_mv
repository.mail.fl_str_mv
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