Scholarlink

<p>We propose an innovative spatial-based multivariate Generalized Poisson regression model to explain the number of outdoor recreation trips to protected areas. The innovation consists of capturing the impact of geographical and environmental similarities across destinations. As a proof of concept, the approach is applied to data obtained from an online survey with 794 respondents sampled from across Italy between October and November 2022. The attention is placed on assessing travellers’ preferences for four popular Italian National Parks: the Parco Nazionale delle Dolomiti Bellunesi, the Parco Nazionale dell’Appennino Tosco-Emiliano, the Parco Nazionale delle Cinque Terre, and the Parco Nazionale dell’Abruzzo, Lazio e Molise. The results corroborate the existence of strong spatial and environmental correlation patterns across the nature sites under assessment. Further, the multivariate approach enhances the predictive accuracy of trip count frequencies relative to simpler models used for comparison.</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

Geographical and environmental dependencies in multivariate count models: Recreation demand for Italian national parks

  • 期刊论文

  • 发表日期：

    2026年1月5日

查看全文
  • Andrea Pellegrini,
  • Riccardo Scarpa,
  • Maria De Salvo,
  • Laura Giuffrida,
  • Giovanni Signorello

摘要

We propose an innovative spatial-based multivariate Generalized Poisson regression model to explain the number of outdoor recreation trips to protected areas. The innovation consists of capturing the impact of geographical and environmental similarities across destinations. As a proof of concept, the approach is applied to data obtained from an online survey with 794 respondents sampled from across Italy between October and November 2022. The attention is placed on assessing travellers’ preferences for four popular Italian National Parks: the Parco Nazionale delle Dolomiti Bellunesi, the Parco Nazionale dell’Appennino Tosco-Emiliano, the Parco Nazionale delle Cinque Terre, and the Parco Nazionale dell’Abruzzo, Lazio e Molise. The results corroborate the existence of strong spatial and environmental correlation patterns across the nature sites under assessment. Further, the multivariate approach enhances the predictive accuracy of trip count frequencies relative to simpler models used for comparison.