Abstract 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.
Calculli et al. (Sat,) studied this question.