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<p>This study investigates the spatial distribution of wildfire counts for Italian municipalities, focusing on some challenges inherent to modelling spatially aggregated areal count data. Leveraging high-resolution satellite-derived fire data aggregated to administrative units, we model spatial dependence and heterogeneity using the Integrated Nested Laplace Approximation (INLA) framework. Based on a single-season case study, the analysis addresses some key modelling issues, including model selection for hierarchical structures through Leave-Group-Out Cross-Validation (LGOCV) and the mitigation of spatial confounding. The results underscore the importance of municipal-level characteristics, such as land use, demographic trends, and socioeconomic conditions, in shaping wildfire patterns across the country.</p>

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Bayesian spatial modelling of satellite-derived wildfire counts across Italian municipalities

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月14日

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  • Crescenza Calculli,
  • Lorena Ricciotti,
  • Alessio Pollice

摘要

This study investigates the spatial distribution of wildfire counts for Italian municipalities, focusing on some challenges inherent to modelling spatially aggregated areal count data. Leveraging high-resolution satellite-derived fire data aggregated to administrative units, we model spatial dependence and heterogeneity using the Integrated Nested Laplace Approximation (INLA) framework. Based on a single-season case study, the analysis addresses some key modelling issues, including model selection for hierarchical structures through Leave-Group-Out Cross-Validation (LGOCV) and the mitigation of spatial confounding. The results underscore the importance of municipal-level characteristics, such as land use, demographic trends, and socioeconomic conditions, in shaping wildfire patterns across the country.