NDLI: Logistic regression models for human-caused wildfire risk estimation: analysing the effect of the spatial accuracy in fire occurrence data

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Logistic regression models for human-caused wildfire risk estimation: analysing the effect of the spatial accuracy in fire occurrence data

Content Provider	Springer Nature Link
Author	Vilar del Hoyo, Lara Martín Isabel, M. Pilar Martínez Vega, F. Javier
Copyright Year	2011
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.
Starting Page	983
Ending Page	996
Page Count	14
File Format	PDF
ISSN	16124669
Journal	Forstwissenschaftliches Centralblatt vereinigt mit Tharandter forstliches Jahrbuch
Volume Number	130
Issue Number	6
e-ISSN	16124677
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-02-10
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Euro-Mediterranean Fire ignition points GIS Kernel interpolation Socio-economic Wildland–urban interface Plant Ecology Forestry Plant Sciences
Content Type	Text
Resource Type	Article
Subject	Plant Science Forestry

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GIS analysis of spatial patterns of human-caused wildfire ignition risk in the SW of Madrid (Central Spain)

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Logistic regression models for human-caused wildfire risk estimation: analysing the effect of the spatial accuracy in fire occurrence data

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Logistic regression models for human-caused wildfire risk estimation: analysing the effect of the spatial accuracy in fire occurrence data