Work

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

Year: 2026

Type: article

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.

Source: Environmental and Ecological Statistics

DOI: 10.1007/s10651-026-00723-8

Authors: Crescenza Calculli, Lorena Ricciotti, Alessio Pollice

Institution: University of Bari Aldo Moro

Language: English

Topic: Spatial and Panel Data Analysis

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $2,290

Funder: Universita degli Studi di Bari Aldo Moro