Urban fire risk evaluation of Xuzhou city based on GIS

Abstract

Urban fire risk evaluation is the basis of optimal distribution of city fire fighting resources. The method of fuzzy analytic hierarchy process is the most commonly-used one in traditional fire risk evaluation. Although the method can deal with qualitative fire risk factors in a quantitative way, it has never included the important geographic factors into the evaluation model, so that only overall comprehensive fire risk index of evaluated region can be attained, which makes evaluation results only have symbolic value of indexes. In fact, the city's fire risk index is not only related with the nature of fire risk sources, but also with their spatial distribution and as well as closely with their climatic factors. In addition, the city's fire risk index shows a certain degree of spatial difference within the entire scope of evaluation. This paper presents a spatial diffusion model based on geographic grid. With the use of the diffusion algorithms, the model figures out the impact weights and contribution values of fire risk on different grids according to the nature and the spatial distribution features of fire risk factors within different regions. Combined with GIS technology, and with new and old urban area of Xuzhou being gridded, diffusion analysis of single fire factor being carried out and overlay analysis of multiple fire factors being conducted, the expected results have been achieved. The results show that the method can acquire the gradient fire risk index of an arbitrary discrete subgoal within the evaluated region. Meanwhile, the results further indicate that the method can display evaluation results through geographic grid and a color grading method, which make assessment results more operational and feasible. The spatial diffusion model based on geographic grid improves the traditional fuzzy analytic hierarchy process, enabling vague problems more concrete and overall problems more dispersed, which provides a new way of thinking for early warnings of fire risk and resource optimization of the city in the future.