Scholarlink

<p>Tourism seasonality is a&#xa0;complex and multidimensional phenomenon shaped by both spatial and temporal dynamics. Despite extensive research, its measurement remains a&#xa0;significant methodological challenge, especially the territorial dimension. A&#xa0;key difficulty lies in the aggregation of spatial data, which introduces spatial compensation that obscures territorial heterogeneity in seasonality and lead to the Modifiable Areal Unit Problem (MAUP). This limitation has been exacerbated by traditional data sources, which often lack spatial granularity. This research analyses tourism seasonality in Spain during 2023, using mobile positioning data (MPD) and the Gini index decomposition across multiple scales: country, region, province, and municipality. Findings reveal that territorial disaggregation is crucial, as it uncovers substantial variations in seasonal patterns and concentration levels that are masked at aggregated scales. To address these challenges, we propose an innovative metric to quantify spatial compensation effects, and three distinct scenarios have been identified, each with distinct managerial implications: high seasonality-low compensation, low seasonality-low compensation, and low seasonality-high compensation. This research advances the methodological framework for analysing tourism seasonality by explicitly addressing the MAUP and highlighting MPD’s value as a&#xa0;data source.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multiscale perspectives on the seasonality of tourism flows in Spain and spatial compensation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月22日

查看全文
  • José Manuel Viera-González,
  • Raúl Hernández-Martín

摘要

Tourism seasonality is a complex and multidimensional phenomenon shaped by both spatial and temporal dynamics. Despite extensive research, its measurement remains a significant methodological challenge, especially the territorial dimension. A key difficulty lies in the aggregation of spatial data, which introduces spatial compensation that obscures territorial heterogeneity in seasonality and lead to the Modifiable Areal Unit Problem (MAUP). This limitation has been exacerbated by traditional data sources, which often lack spatial granularity. This research analyses tourism seasonality in Spain during 2023, using mobile positioning data (MPD) and the Gini index decomposition across multiple scales: country, region, province, and municipality. Findings reveal that territorial disaggregation is crucial, as it uncovers substantial variations in seasonal patterns and concentration levels that are masked at aggregated scales. To address these challenges, we propose an innovative metric to quantify spatial compensation effects, and three distinct scenarios have been identified, each with distinct managerial implications: high seasonality-low compensation, low seasonality-low compensation, and low seasonality-high compensation. This research advances the methodological framework for analysing tourism seasonality by explicitly addressing the MAUP and highlighting MPD’s value as a data source.