Machine learning techniques as a tool for predicting <scp>overtourism</scp>: The case of Spain

Perles‐Ribes, José Francisco; Rodríguez, Ana Belén Ramón; Moreno‐Izquierdo, Luis; Devesa, María Jesús Such

doi:10.1002/jtr.2383

Cited by 18 publications

(8 citation statements)

References 63 publications

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“…Other evident dichotomies besides the clear example of the sector's sustainability can be highlighted. For instance, the psychological benefits for social well-being derived from rest and leisure are evident (Neal et al, 2007;Filep, 2011), yet stress is also generated among residents of destinations where tourism leads to overcrowding (Jordan & Vogt, 2017;Perles et al, 2020). In terms of economic development, some authors highlight tourism's positive effects in countries such as Spain (Cortes-Jimenez & Pulina, 2010;Perles-Ribes et al, 2017), while others point out the divergence that specialization in tourism activity is creating among European countries (Haller et al, 2020).…”

Section: The Relationship Between Tourism and Economic Development: T...mentioning

confidence: 99%

Tourism Growth and Economic Development

Perles-Ribes,

Moreno-Izquierdo,

Ramón-Rodríguez

et al. 2024

Tourism (Zagreb, Online)

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This article explores the complex relationship between tourism growth and economic development from academic and non-academic perspectives. The former mainly highlights the positive influence of tourism while recognizing biases and limitations that may arise from using different methodologies, variables, or temporary scenarios in research. However, non-academic stakeholders, such as the media and politicians, offer unverified and biased opinions that can influence public perceptions and lead to failed investments. This article provides recommendations to foster a balanced conversation between the two perspectives, with a broader dissemination of academic results and a recognition of the difficulties in analyzing the relationship between tourism and economic development, such as the mobility of stakeholders.

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Section: The Relationship Between Tourism and Economic Development: T...mentioning

confidence: 99%

Tourism Growth and Economic Development

Perles-Ribes,

Moreno-Izquierdo,

Ramón-Rodríguez

et al. 2024

Tourism (Zagreb, Online)

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“…Equation (3) shows Inverse Distance Weighting, where the weight of a neighbor is inversely proportional to its distance from the query objects. Equation (4) shows that WkNN introduces the concept of assigning weights to the neighboring data points based on their proximity to the query point. These weights are used to influence the final classification or prediction.…”

Section: Weighted Knn (Wknn)mentioning

confidence: 99%

“…For instance, the tourism industry employed 10% of the world's workforce (about 300 million people) in 2016, and this percentage may reach 11.4% by 2027 [3]. In 2018, a total of about 1.4 billion tourists were recorded globally [4]. However, tourism also suffers diverse natural disasters, such as floods, since it mostly relies on the natural environment, like being near water [5].…”

Section: Introductionmentioning

confidence: 99%

A Weighted k-Nearest-Neighbors-Based Spatial Framework of Flood Inundation Risk for Coastal Tourism—A Case Study in Zhejiang, China

Liu,

Tan,

Liu

2023

IJGI

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Flood inundation causes socioeconomic losses for coastal tourism under climate extremes, progressively attracting global attention. Predicting, mapping, and evaluating the flood inundation risk (FIR) is important for coastal tourism. This study developed a spatial tourism-aimed framework by integrating a Weighted k Nearest Neighbors (WkNN) algorithm, geographic information systems, and environmental indexes, such as precipitation and soil. These model inputs were standardized and weighted using inverse distance calculation and integrated into WkNN to infer the regional probability and distribution of the FIR. Zhejiang province, China, was selected as a case study. The evaluation results were mapped to denote the likelihood of an FIR, which was then validated by the historical Maximum Inundation Extent (MIE) extracted from the World Environment Situation Room. The results indicated that 80.59% of the WkNN results reasonably confirmed the MIE. Among the matched areas, 80.14%, 90.13%, 65.50%, and 84.14% of the predicted categories using WkNN perfectly coincided with MIE at high, medium, low, and very low risks, respectively. For the entire study area, approximately 2.85%, 64.83%, 10.8%, and 21.51% are covered by a high, medium, low, and very low risk of flood inundation. Precipitation and elevation negatively contribute to a high-medium risk. Drainage systems positively alleviate the regional stress of the FIR. The results of the evaluation illustrate that in most inland areas, some tourism facilities are located in high-medium areas of the FIR. However, most tourism facilities in coastal cities are at low or very low risk, especially from Hangzhou-centered northern coastal areas to southern Wenzhou areas. The results can help policymakers make appropriate strategies to protect coastal tourism from flood inundation. Moreover, the evaluation accuracy of WkNN is higher than that of kNN in FIR. The WkNN-based framework provides a reasonable method to yield reliable results for assessing FIR. The framework can also be extended to other risk-related research under climate change.

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“…In the light of this issue, and although great eff orts have been made to attempt to measure the degree of saturation of destinations (McKinsey & Company, 2017;Peeters et. al., 2018) through diff erent methods, including machine learning techniques (Perles-Ribes et al, 2020), the challenge for researchers continues to be the creation of indicators that are able to anticipate the loss of tourism competitiveness in the destinations and the resulting problems of overtourism. Th ese indicators should help policy makers to make decisions and correct a possible negative trend in terms of image, sustainability and profi tability.…”

Section: Proposition 1: Study Of the Sustainability Of Tourist Destinations In The Light Of The Challenge Of Tourist Saturationmentioning

confidence: 99%

Competitiveness, Intelligence and Sustainability in Consolidated Destinations

Rodríguez

Devesa

Perles‐Ribes

et al. 2021

Tourism (Zagreb, Online)

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Competitiveness, sustainability and innovation have been studied by the tourism literature at least since the 1990s. In spite of the advances made in the comprehension of these concepts, the dynamic nature of the industry continually generates new practical challenges for the public and private agents of the sector and scientific challenges for tourism researchers. Currently, globalization and digital progress, the emergence of new marketing platforms, the increased saturation of certain destinations and the COVID-19 pandemic are significantly affecting the competitiveness and sustainability of tourism activity. This calls for a scientific review of what we know about the competitiveness and sustainability of the sector and an analysis of its feasibility in this new competitive environment that is constantly and intensely transforming, even more so in the post-COVID19 era. The objective of this article is to conceptually analyze the state of this question, using Spain as a case study, as it has been the global leader in tourism competitiveness in recent years. The high level of consolidation of the Spanish tourism sector enables us to make a more in-depth analysis of the implications of these phenomena and to extract practical lessons for destinations that are in the more initial phases of their life cycles.

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Machine learning techniques as a tool for predicting overtourism: The case of Spain

Cited by 18 publications

References 63 publications

Tourism Growth and Economic Development

Tourism Growth and Economic Development

A Weighted k-Nearest-Neighbors-Based Spatial Framework of Flood Inundation Risk for Coastal Tourism—A Case Study in Zhejiang, China

Competitiveness, Intelligence and Sustainability in Consolidated Destinations

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