Do Factors Causing Wildfires Vary in Space? Evidence from Geographically Weighted Regression. Koutsias, N., Mart́ınez-Fernández, J., & Allgöwer, B. 47(2):221–240. ![Do Factors Causing Wildfires Vary in Space? Evidence from Geographically Weighted Regression [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex This paper describes the results of a geo-statistical analysis carried out at the provincial level in Southern Europe to model wildfire occurrence from socio-economic and demographic indicators together with land cover and agricultural statistics. We applied a classical ordinary least squares (OLS) linear regression together with a geographically weighted regression (GWR) to explain long-term wild-fire occurrence patterns (mean annual density of $>$1 ha fires). The explanatory power of the OLS model increased from 52\,% to 78\,% as a result of the non-constant relationships between fire occurrence and the underlying explanatory variables throughout the Mediterranean Basin. The global model we developed (i.e., OLS regression) was not sufficient to fully describe the underlying causal factors in wildfire occurrence modeling. Indeed, local approaches (i.e., GWR) can complement the global model in overcoming the problem of non-stationarity or missing variables. Our results confirm the importance of agrarian activities, land abandonment, and development processes as underlying factors of fire occurrence. The identification of regions with spatially varying relationships can contribute to the better understanding of the fire problem, especially over large geographic areas, while at the same time recognizing its local character. This can be very important for fire management and policy.
@article{koutsiasFactorsCausingWildfires2013,
title = {Do Factors Causing Wildfires Vary in Space? {{Evidence}} from Geographically Weighted Regression},
author = {Koutsias, Nikos and Mart́ınez-Fernández, Jesús and Allgöwer, Britta},
date = {2013-05},
journaltitle = {GIScience \& Remote Sensing},
volume = {47},
pages = {221--240},
issn = {1548-1603},
doi = {10.2747/1548-1603.47.2.221},
url = {https://doi.org/10.2747/1548-1603.47.2.221},
abstract = {This paper describes the results of a geo-statistical analysis carried out at the provincial level in Southern Europe to model wildfire occurrence from socio-economic and demographic indicators together with land cover and agricultural statistics. We applied a classical ordinary least squares (OLS) linear regression together with a geographically weighted regression (GWR) to explain long-term wild-fire occurrence patterns (mean annual density of {$>$}1 ha fires). The explanatory power of the OLS model increased from 52\,\% to 78\,\% as a result of the non-constant relationships between fire occurrence and the underlying explanatory variables throughout the Mediterranean Basin. The global model we developed (i.e., OLS regression) was not sufficient to fully describe the underlying causal factors in wildfire occurrence modeling. Indeed, local approaches (i.e., GWR) can complement the global model in overcoming the problem of non-stationarity or missing variables. Our results confirm the importance of agrarian activities, land abandonment, and development processes as underlying factors of fire occurrence. The identification of regions with spatially varying relationships can contribute to the better understanding of the fire problem, especially over large geographic areas, while at the same time recognizing its local character. This can be very important for fire management and policy.},
keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-14684725,agricultural-abandonment,agricultural-land,agricultural-resources,anthropogenic-impacts,mediterranean-region,southern-europe,spatial-pattern,variability,wildfires},
number = {2}
}
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