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.
Highlights
Fire is a widespread phenomenon in modern life
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Just as for the spatial autocorrelation model (SAC) model, five intermediate Random Forest (RF) models were calculated by using the same training sets and the importance of each predictor was obtained in order to select the final RF model
Summary
Fire is a widespread phenomenon in modern life. In China in 2015, 1742 people were killed in fires, with an economic loss of nearly $0.6 billion [1]. Few studies were done at the city scale to explain and predict of the occurrence of infrastructure fire, which may lead to a lack of efficient management for the potential fire risks hidden in a city It has become exceptionally arduous for governments and policy makers due to the complexity of risk prediction based on the integrated correlations among multiple socioeconomic predictors. The residual term of the adopted models in this paper may still be spatially auto-correlative, which betrays the statistical assumption that the model could explain the spatial structure efficiently In light of this reason, spatial econometric models (SE) including the spatial Durbin model (SDM), spatial autocorrelation model (SAC), spatial lagging model (SLM), and spatial error model (SEM) have the potential to offer new insights into the modeling of fire occurrence, considering spatial autocorrelation in the response variable, explanatory variables or random error terms [15]. The correlations between residuals and common predictors in both models, are presented and danisdcudsissecudsisneddientadile.taTilh. eThgeragprhasphosf othf ethleikleikliehliohoododofofif rfeireococcucurrrernencecepprereddicicteteddbbyyeeaacchh mmooddeell aarree aallssoo aa ddiirreecctt ddeemmoonnssttrraattiioonn
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