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  "notes": "This paper identifies human factors associated with high 
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forest fire risk in Spain and analyses the spatial distribution of 
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forest fire risk in Spain and analyses the spatial distribution of 
            
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fire occurrence in the country. The spatial units were 6,066 
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fire occurrence in the country. The spatial units were 6,066 
            
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municipalities of the Spanish peninsular territory and Balearic 
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municipalities of the Spanish peninsular territory and Balearic 
            
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Islands. The study covered a 13-year series of fire occurrence data. 
            
84
One hundred and eight variables were generated and input to a 
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One hundred and eight variables were generated and input to a 
            
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dedicated Geographic Information System (GIS) to model different 
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dedicated Geographic Information System (GIS) to model different 
            
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factors related to fire ignition. After exploratory analysis, 29 were 
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factors related to fire ignition. After exploratory analysis, 29 were 
            
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selected to build a predictive model of human fire ignition using 
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selected to build a predictive model of human fire ignition using 
            
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logistic regression analysis. The binary model estimated the 
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logistic regression analysis. The binary model estimated the 
            
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probability of high or low occurrence of forest fires, as defined by 
89
probability of high or low occurrence of forest fires, as defined by 
            
90
an ignition danger index that is currently used by the Spanish forest 
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an ignition danger index that is currently used by the Spanish forest 
            
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service (number of fires divided by forest area in each municipality). 
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service (number of fires divided by forest area in each municipality). 
            
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Thirteen explanatory variables were identified by the model.",
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forest fire risk in Spain and analyses the spatial distribution of 
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forest fire risk in Spain and analyses the spatial distribution of 
            
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fire occurrence in the country. The spatial units were 6,066 
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fire occurrence in the country. The spatial units were 6,066 
            
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municipalities of the Spanish peninsular territory and Balearic 
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Islands. The study covered a 13-year series of fire occurrence data. 
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Islands. The study covered a 13-year series of fire occurrence data. 
            
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One hundred and eight variables were generated and input to a 
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One hundred and eight variables were generated and input to a 
            
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dedicated Geographic Information System (GIS) to model different 
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dedicated Geographic Information System (GIS) to model different 
            
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factors related to fire ignition. After exploratory analysis, 29 were 
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factors related to fire ignition. After exploratory analysis, 29 were 
            
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selected to build a predictive model of human fire ignition using 
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selected to build a predictive model of human fire ignition using 
            
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logistic regression analysis. The binary model estimated the 
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logistic regression analysis. The binary model estimated the 
            
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probability of high or low occurrence of forest fires, as defined by 
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probability of high or low occurrence of forest fires, as defined by 
            
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an ignition danger index that is currently used by the Spanish forest 
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an ignition danger index that is currently used by the Spanish forest 
            
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service (number of fires divided by forest area in each municipality). 
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service (number of fires divided by forest area in each municipality). 
            
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Thirteen explanatory variables were identified by the model. They were 
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Thirteen explanatory variables were identified by the model. They were 
            
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forest fire risk in Spain and analyses the spatial distribution of 
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forest fire risk in Spain and analyses the spatial distribution of 
            
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Islands. The study covered a 13-year series of fire occurrence data. 
            
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One hundred and eight variables were generated and input to a 
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factors related to fire ignition. After exploratory analysis, 29 were 
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factors related to fire ignition. After exploratory analysis, 29 were 
            
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selected to build a predictive model of human fire ignition using 
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selected to build a predictive model of human fire ignition using 
            
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logistic regression analysis. The binary model estimated the 
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logistic regression analysis. The binary model estimated the 
            
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probability of high or low occurrence of forest fires, as defined by 
            
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an ignition danger index that is currently used by the Spanish forest 
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an ignition danger index that is currently used by the Spanish forest 
            
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service (number of fires divided by forest area in each municipality). 
            
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Thirteen explanatory variables were identified by the model."
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