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Spatial modelling of socioeconomic data to understand patterns of human-caused wildfire ignition risk in the SW of Madrid (central Spain)

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  • Romero-Calcerrada, R.
  • Barrio-Parra, F.
  • Millington, J.D.A.
  • Novillo, C.J.

Abstract

The majority of wildfires in the Mediterranean Basin are caused directly or indirectly by human activity. Many biophysical and socioeconomic factors have been used in quantitative analyses of wildfire risk. However, the importance and effects of socioeconomic factors in spatial modelling have been given inadequate attention. In this paper, we use different approaches to spatially model our data to examine the influence of human activity on wildfire ignition in the south west of the Madrid region, central Spain. We examine the utility of choropleth and dasymetric mapping with both Euclidean and functional distance surfaces for two differently defined wildfire seasons. We use a method from Bayesian statistics, the Weights of Evidence model, and produce ten predictive maps of wildfire risk: (1) five maps for a two-month fire season combining datasets of evidence variables and (2) five maps for the four-month fire season using the same dataset combinations. We find that the models produced from a choropleth mapping approach with spatial variables using Euclidian and functional distance surfaces are the best of the ten models. Results indicate that spatial patterns of wildfire ignition are strongly associated with human access to the natural landscape. We suggest the methods and results presented will be useful to optimize wildfire prevention resources in areas where human activity and the urban–forest interface are important factors for wildfire ignition.

Suggested Citation

  • Romero-Calcerrada, R. & Barrio-Parra, F. & Millington, J.D.A. & Novillo, C.J., 2010. "Spatial modelling of socioeconomic data to understand patterns of human-caused wildfire ignition risk in the SW of Madrid (central Spain)," Ecological Modelling, Elsevier, vol. 221(1), pages 34-45.
    • Handle: RePEc:eee:ecomod:v:221:y:2010:i:1:p:34-45
    DOI: 10.1016/j.ecolmodel.2009.08.008
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    References listed on IDEAS

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    1. Mercer, D. Evan & Prestemon, Jeffrey P., 2005. "Comparing production function models for wildfire risk analysis in the wildland-urban interface," Forest Policy and Economics, Elsevier, vol. 7(5), pages 782-795, August.
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    1. Chao Song & Mei-Po Kwan & Weiguo Song & Jiping Zhu, 2017. "A Comparison between Spatial Econometric Models and Random Forest for Modeling Fire Occurrence," Sustainability, MDPI, vol. 9(5), pages 1-21, May.
    2. Miranda, Jonathan da Rocha & Juvanhol, Ronie Silva & da Silva, Rosane Gomes, 2023. "Use of maximum entropy to improve validation and prediction of active fires in a Brazilian savanna region," Ecological Modelling, Elsevier, vol. 475(C).
    3. Yang, Xue-Qing & Kodikara, Gayantha R.L. & Luedeling, Eike & Yang, Xue-Fei & He, Jun & Liu, Pei-gui & Xu, Jian-Chu, 2012. "Looking below the ground: Prediction of Tuber indicum habitat using the Weights of Evidence method," Ecological Modelling, Elsevier, vol. 247(C), pages 27-39.
    4. Sadasivuni, R. & Cooke, W.H. & Bhushan, S., 2013. "Wildfire risk prediction in Southeastern Mississippi using population interaction," Ecological Modelling, Elsevier, vol. 251(C), pages 297-306.
    5. Margherita Carlucci & Ilaria Zambon & Andrea Colantoni & Luca Salvati, 2019. "Socioeconomic Development, Demographic Dynamics and Forest Fires in Italy, 1961–2017: A Time-Series Analysis," Sustainability, MDPI, vol. 11(5), pages 1-17, March.
    6. Marcos Rodrigues & Adrián Jiménez & Juan de la Riva, 2016. "Analysis of recent spatial–temporal evolution of human driving factors of wildfires in Spain," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(3), pages 2049-2070, December.

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