Logistic regression models for human-caused wildfire risk estimation: analysing the effect of the spatial accuracy in fire occurrence data |
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| Authors: | Lara Vilar del Hoyo M Pilar Martín Isabel F Javier Martínez Vega |
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| Institution: | (1) IES, Joint Research Centre of the European Commission, TP 261, Via E. Fermi 27492, 21027 Ispra, Va, Italy;(2) Centre for Human and Social Sciences, Spanish Council for Scientific Research, Albasanz 26-28, 28037 Madrid, Spain |
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| Abstract: | About 90% of the wildland fires occurred in Southern Europe are caused by human activities. In spite of these figures, the
human factor hardly ever appears in the definition of operational fire risk systems due to the difficulty of characterising
it. This paper describes two spatially explicit models that predict the probability of fire occurrence due to human causes
for their integration into a comprehensive fire risk–mapping methodology. A logistic regression technique at 1 × 1 km grid
resolution has been used to obtain these models in the region of Madrid, a highly populated area in the centre of Spain. Socio-economic
data were used as predictive variables to spatially represent anthropogenic factors related to fire risk. Historical fire
occurrence from 2000 to 2005 was used as the response variable. In order to analyse the effects of the spatial accuracy of
the response variable on the model performance (significant variables and classification accuracy), two different models were
defined. In the first model, fire ignition points (x, y coordinates) were used as response variable. This model was compared with another one (Kernel model) where the response variable
was the density of ignition points and was obtained through a kernel density interpolation technique from fire ignition points
randomly located within a 10 × 10 km grid, which is the standard spatial reference unit established by the Spanish Ministry
of Environment, Rural and Marine Affairs to report fire location in the national official statistics. Validation of both models
was accomplished using an independent set of fire ignition points (years 2006–2007). For the validation, we used the area
under the curve (AUC) obtained by a receiver-operating system. The first model performs slightly better with a value of AUC
of 0.70 as opposed to 0.67 for the Kernel model. Wildland–urban interface was selected by both models with high relative importance. |
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