PROGRESS IN GEOGRAPHY ›› 2014, Vol. 33 ›› Issue (7): 958-968.doi: 10.11820/dlkxjz.2014.07.011

• Orginal Article • Previous Articles     Next Articles

Spatial analysis of fire-influencing factors in Henan Province

Haijun ZHANG()   

  1. School of Environmental Science and Tourism, Nanyang Normal University, Nanyang 473061, Henan, China
  • Online:2014-07-25 Published:2014-07-25


The spatial relationships between fire events and fire-influencing factors have important implications for fire managers and scientifically revealing these relationships is therefore significant for management purposes. The spatial stationary relationships between fire events and fire-influencing factors considered by previous fire risk studies contradict the fact that fire events and their quantifiable influencing factors are always characterized by spatial heterogeneity. If the intrinsically non-stationary relationships between fire events and fire-influencing factors are modeled by some stationary models, misleading and even erroneous conclusions can be drawn, which hamper fire prevention operations. In this study, logistic geographically weighted regression (LGWR) that can account for local variations of spatial relationships between fire events and fire-influencing factors was employed to analyze the influences of different fire-influencing factors on fire events in the high risk season (September and October) from 2002 to 2012 in Henan Province. The independent variables of the model include altitude (Al), slope (Sl), distance to the nearest village (Dv), distance to the nearest path (Dp), land surface temperature (LST), Normalized Difference Vegetation Index (NDVI), and global vegetation moisture index (GVMI); and the binary dependent variable is monthly fire presence, with 1 representing presence and 0 representing absence. A training subset derived from spatial random sampling was created and potential multicollinearity among the independent variables was excluded, and then a LGWR fire probability model was developed using the GWR 4.0 software. The reliability and discrimination capacity of the developed fire probability spatial model was evaluated using a testing subset and an independent validation subset and the results show good model performance. The model was used for fire-influencing factor analysis in the next step. After delineating and overlaying the significant areas of the non-stationary fire-influencing factors, seven fire prevention regions were identified in Henan Province. The relative importance of the non-stationary fire-influencing factors was evaluated by comparing the absolute values of their estimated coefficients spatially. The results indicate that: I) The influences of Sl, Dv, LST, NDVI and GVMI on fire events present significant spatial variability, whereas the influences of Al and Dp exhibit insignificant spatial variability in Henan Province. II) The influences of LST and NDVI on fire events are significant globally in Henan Province, whereas the influences of Sl, Dv and GVMI are only significant locally. The sites most strongly influenced by LST are mainly Nanyang, Zhumadian, Xinyang and their contiguous areas. The sites most strongly influenced by vegetation cover (NDVI) are primarily Zhoukou, Xinyang, Luohe, Xuchang, Zhumadian and Shangqiu. In Xinyang and southeast Zhumadian, fire events are most strongly influenced by Sl, while in Luoyang this factor is Dv, and in Zhoukou and the adjacent area of Luoyang, Nanyang and Pingdingshan, it is GVMI. III) This study demonstrates the usefulness of LGWR for exploring local variations of fire-influencing factors and for examining the validity of a global fire probability model. The practical implication of spatial analysis of fire-influencing factors resulted from LGWR is that different fire prevention policies and emphases should be formulated for each of the seven fire prevention regions. Because of such heterogeneity, fire prevention policies need to take into consideration local conditions.

Key words: fire-influencing factors, Geographically Weighted Regression (GWR), logistic regression, local model, Henan Province

CLC Number: 

  • X43