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在进行灾害预测时,导致灾害产生的影响因子在空间上的关联程度对预测结果有较大影响。文章提出一种量化灾害影响因子间关联程度的方法,即测量灾害影响因子的空间自相关性;并以重庆市东北部五县区域内的森林覆盖率为例,进行森林覆盖率的空间自相关性分析。研究通过地理信息系统,对相关数据进行预处理,构建数值表面模型,并通过全局Moran’s I指数检验,得出该地区的森林覆盖呈现聚集式的分布,通过局部Moran’s I和局部G统计量得出属性值高低的聚类及其相互间关系。研究结果表明,该地区的森林覆盖情况呈现显著的空间自相关,且存在一定的聚集性,在利用该影响因子进行灾害分析与预测时,应将空间自相关同时作为一项影响因素。
In the process of disaster prediction, the spatial correlation of the impact factors leading to disasters has a greater impact on the forecast results. This paper presents a method to quantify the degree of association between disaster impact factors, that is to measure the spatial autocorrelation of disaster impact factors. Taking the forest coverage in five counties in northeastern Chongqing as an example, the spatial autocorrelation of forest coverage Sexual analysis. Through GIS, the related data are preprocessed to construct the numerical surface model, and the global Moran’s I index test results show that the forest cover in the area shows an aggregated distribution, which is obtained from the local Moran’s I and the local G statistics Clustering of attribute values and their relationship to each other. The results show that there is a significant spatial autocorrelation of the forest cover in the area, and there is a certain degree of aggregation. When making use of the impact factor for disaster analysis and prediction, spatial autocorrelation should be taken as an influencing factor at the same time.