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

Crescenza Calculli et al.

Environmental and Ecological Statistics2026https://doi.org/10.1007/s10651-026-00723-8article
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0.50

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.

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https://doi.org/https://doi.org/10.1007/s10651-026-00723-8

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@article{crescenza2026,
  title        = {{Bayesian spatial modelling of satellite-derived wildfire counts across Italian municipalities}},
  author       = {Crescenza Calculli et al.},
  journal      = {Environmental and Ecological Statistics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1007/s10651-026-00723-8},
}

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

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0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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