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A Comparison between Spatial Econometric Models and Random Forest for Modeling Fire Occurrence

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  • Chao Song

    (State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230026, China)

  • Mei-Po Kwan

    (Department of Geography and Geographic Information Science, University of Illinois at Urbana-Champaign, Champaign, IL 61820, USA
    Department of Human Geography and Spatial Planning, Faculty of Geosciences, Utrecht University, P.O. Box 80125, 3508 TC Utrecht, The Netherlands)

  • Weiguo Song

    (State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230026, China)

  • Jiping Zhu

    (State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230026, China)

Abstract

Fire occurrence, which is examined in terms of fire density (number of fire/km2) in this paper, has a close correlation with multiple spatiotemporal factors that include environmental, physical, and other socioeconomic predictors. Spatial autocorrelation exists widely and should be considered seriously for modeling the occurrence of fire in urban areas. Therefore, spatial econometric models (SE) were employed for modeling fire occurrence accordingly. Moreover, Random Forest (RF), which can manage the nonlinear correlation between predictors and shows steady predictive ability, was adopted. The performance of RF and SE models is discussed. Based on historical fire records of Hefei City as a case study in China, the results indicate that SE models have better predictive ability and among which the spatial autocorrelation model (SAC) is the best. Road density influences fire occurrence the most for SAC, while network distance to fire stations is the most important predictor for RF; they are selected in both models. Semivariograms are employed to explore their abilities to explain the spatial structure of fire occurrence, and the result shows that SAC works much better than RF. We give a further explanation for the generation of residuals between fire density and the common predictors in both models. Therefore, decision makers can make use of our conclusions to manage fire safety at the city scale.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jsusta:v:9:y:2017:i:5:p:819-:d:98624
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    References listed on IDEAS

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    1. 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.
    2. Rajagopal, 2014. "The Human Factors," Palgrave Macmillan Books, in: Architecting Enterprise, chapter 9, pages 225-249, Palgrave Macmillan.
    3. 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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    Cited by:

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    2. Dawid Siwicki, 2021. "The Application of Machine Learning Algorithms for Spatial Analysis: Predicting of Real Estate Prices in Warsaw," Working Papers 2021-05, Faculty of Economic Sciences, University of Warsaw.
    3. Abdullah Al Saim & Mohamed H. Aly, 2022. "Machine Learning for Modeling Wildfire Susceptibility at the State Level: An Example from Arkansas, USA," Geographies, MDPI, vol. 2(1), pages 1-17, January.
    4. Jiansong Wu & Zhuqiang Hu & Jinyue Chen & Zheng Li, 2018. "Risk Assessment of Underground Subway Stations to Fire Disasters Using Bayesian Network," Sustainability, MDPI, vol. 10(10), pages 1-21, October.
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    6. Saeedeh Eskandari & Mahdis Amiri & Nitheshnirmal Sãdhasivam & Hamid Reza Pourghasemi, 2020. "Comparison of new individual and hybrid machine learning algorithms for modeling and mapping fire hazard: a supplementary analysis of fire hazard in different counties of Golestan Province in Iran," 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. 104(1), pages 305-327, October.

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