Spatio-Temporal Marked Point Process Model to Understand Forest Fires in the Mediterranean Basin

Óscar Rodríguez Rivera (), Juncal Espinosa, Javier Madrigal, Marta Blangiardo and Antonio López-Quílez
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Óscar Rodríguez Rivera: University of Exeter
Juncal Espinosa: University of Trás-os-Montes e Alto Douro
Javier Madrigal: ICIFOR-INIA (CSIC)
Marta Blangiardo: Imperial College London
Antonio López-Quílez: Universitat de València

Journal of Agricultural, Biological and Environmental Statistics, 2025, vol. 30, issue 3, No 6, 700-729

Abstract: Abstract Understanding and predicting forest fires have proved a highly difficult endeavour, which requires extending and adapting complex models used in different fields. Here, we apply a marked point process approach, commonly used in ecology, which uses multiple Gaussian random fields to represent dynamics of Mediterranean forest fires in a spatio-temporal distribution model. Inference is carried out using Integrated Nested Laplace Approximation (INLA) with inlabru, an accessible and computationally efficient approach for Bayesian hierarchical modelling, which is not yet widely used in species distribution models. Using the marked point process approach, intensity of forest fires and dispersion were predicted using socioeconomic factors and environmental and fire-related variables. This demonstrates the advantage of complex model components in accounting for spatio-temporal dynamics that are not explained by environmental variables. Introduction of spatio-temporal marked point process can provide a more realistic perspective of a system, which is of particular importance for a practical and impact-focused worldwide problem such as forest fires. Supplementary materials accompanying this paper appear online.

Keywords: INLA; inlabru; Marked point process; Spatio-temporal model; Forest fires (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s13253-024-00617-x

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