Abstract
Abstract. Humans are responsible for most forest fires in Europe, but anthropogenic factors behind these events are still poorly understood. We tried to identify the driving factors of human-caused fire occurrence in Spain by applying two different statistical approaches. Firstly, assuming stationary processes for the whole country, we created models based on multiple linear regression and binary logistic regression to find factors associated with fire density and fire presence, respectively. Secondly, we used geographically weighted regression (GWR) to better understand and explore the local and regional variations of those factors behind human-caused fire occurrence. The number of human-caused fires occurring within a 25-yr period (1983–2007) was computed for each of the 7638 Spanish mainland municipalities, creating a binary variable (fire/no fire) to develop logistic models, and a continuous variable (fire density) to build standard linear regression models. A total of 383 657 fires were registered in the study dataset. The binary logistic model, which estimates the probability of having/not having a fire, successfully classified 76.4% of the total observations, while the ordinary least squares (OLS) regression model explained 53% of the variation of the fire density patterns (adjusted R2 = 0.53). Both approaches confirmed, in addition to forest and climatic variables, the importance of variables related with agrarian activities, land abandonment, rural population exodus and developmental processes as underlying factors of fire occurrence. For the GWR approach, the explanatory power of the GW linear model for fire density using an adaptive bandwidth increased from 53% to 67%, while for the GW logistic model the correctly classified observations improved only slightly, from 76.4% to 78.4%, but significantly according to the corrected Akaike Information Criterion (AICc), from 3451.19 to 3321.19. The results from GWR indicated a significant spatial variation in the local parameter estimates for all the variables and an important reduction of the autocorrelation in the residuals of the GW linear model. Despite the fitting improvement of local models, GW regression, more than an alternative to "global" or traditional regression modelling, seems to be a valuable complement to explore the non-stationary relationships between the response variable and the explanatory variables. The synergy of global and local modelling provides insights into fire management and policy and helps further our understanding of the fire problem over large areas while at the same time recognizing its local character.
Highlights
Instead of modelling only the high versus low occurrence, in this paper we have addressed two aspects of fire occurrence: (i) fire presence/absence and (ii) fire density, using a longer historical time period (25 yr versus 13) for both
After collinearity analysis we decided not to introduce the variables “slope” and “population occupied in agriculture” into the regression procedure
The stepwise procedure for the binary logistic regression selected 9 significant variables for the final model, which successfully classified 76.4 % of the total observations using the estimated optimal cut-off point of 0.91, which corresponds to the intersection of the two lines in which sensitivity and specificity are equal (Vasconcelos et al, 2001)
Summary
The work presented here is an extension of previous research (Martınez et al, 2009) that showed how the rate of humancaused fires in Spain can be predicted and explained from socioeconomic and geographic variables, assuming spatially stationary processes. Instead of modelling only the high versus low occurrence, in this paper we have addressed two aspects of fire occurrence: (i) fire presence/absence and (ii) fire density, using a longer historical time period (25 yr versus 13) for both. For these two aspects we built two predictive “global” models at the national scale using two “classical” regression approaches: OLS linear regression to explain long-term fire density patterns and, complementarily, a binary logistic model to define the existing underlying factors behind fire presence and to better understand why in some of the municipalities no fires have been observed during the studied period. The terms “ordinary” and “classical” are used here to represent the default regression model in many statistical software packages, in contrast to other specific models like GWR
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