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Modelling long-term fire occurrence factors in Spain by accounting for local variations with geographically weighted regression

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Figure

Fig. 1. Map and histograms of dependent variables. Spatial distribution of the fire density in the Spanish municipalities with more than 1 event(A)
Fig. 2. Municipalities where prediction and observation data did not agree; either fire is predicted when it is not observed (overestimated) orfire is not predicted when it is observed (underestimated)
Fig. 3. Residual analysis of the OLS regression model: scatterplots between observed and predicted observations (A), histogram data plotsof the standardized residuals (B), normal Q − Q plot of the standardized residuals (C) and map of the standardized residuals (D).
Fig. 4. Model fitting maps: local squared R for GW lineal (left) and GW logistic (right).
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