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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
ABDC A
Weight
0.50

What the paper says

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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Evidence weight

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

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.