Modeling the spatial variation of the explanatory factors of human-caused wildfires in Spain using geographically weighted logistic regression

Forest fires are one of the main factors transforming landscapes and natural environments in a wide variety of ecosystems. The impacts of fire occur both on a global scale, with increasing emissions of greenhouse gases, and on a local scale, with land degradation, biodiversity loss, property damage, and loss of human lives. Improvements and innovations in fire risk assessment contribute to reducing these impacts. This study analyzes the spatial variation in the explanatory factors of human-caused wildfires in continental Spain using logistic regression techniques within the framework of geographically weighted regression models (GWR). GWR methods are used to model the varying spatial relationships between human-caused wildfires and their explanatory variables. Our results suggest that high fire occurrence rates are mainly linked to wildland–agricultural interfaces and wildland–urban interfaces. The mapping of explanatory factors also evidences the importance of other variables of linear deployment such as power lines, railroads, and forestry tracks. Finally, the GWLR model gives an improved calculation of the probabilities of wildfire occurrence, both in terms of accuracy and goodness of fit, compared to global regression models. •This work explores the spatial variation of explanatory factors of human wildfires.•Binary logistic regression is used within the context of GWR techniques.•There is high spatial variability in the explanatory factors of human-caused wildfires.•WAI seems to be the main factor linked to human-caused wildfires in Spain.