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| Titel |
Modelling long-term fire occurrence factors in Spain by accounting for local variations with geographically weighted regression |
| VerfasserIn |
J. Martínez-Fernández, E. Chuvieco, N. Koutsias |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1561-8633
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| Digitales Dokument |
URL |
| Erschienen |
In: Natural Hazards and Earth System Science ; 13, no. 2 ; Nr. 13, no. 2 (2013-02-11), S.311-327 |
| Datensatznummer |
250017570
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| Publikation (Nr.) |
 copernicus.org/nhess-13-311-2013.pdf |
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| Zusammenfassung |
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. |
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