大气污染物SO_2空间相关性的空间集聚分析

Spatial agglomeration analysis of spatial correlation of atmospheric pollutant SO_2

以东北三省2017年的大气污染物SO_2为研究对象,通过全局指标(全局Moran指数、Geary系数)、区域型指标(Moran’s I、局部Geary’s C、局部Getis’s G)等,对SO_2的空间聚集情况进行分析计算,比较两种指标的探测结果。结果表明,在全局型空间自相关的分析中,Moran指数、Geary’s C两个指标均表明东北三省SO_2存在显著的空间自相关性;Moran散点图、LISA集聚图、局部G系数集聚图等均揭示了东北地区36个地级市SO_2的局部空间相关性,即低值集聚区(冷点)主要集中在研究区东部,(热点)高值集聚区集中在研究区的西南部;通过对两种指数的分析可发现,在研究区的西南部,营口、大连、铁岭3个地区在Moran指数中为低-高集聚区,黑河为不相关地区,但在局部G系数中,营口、大连、铁岭为热点(高-高集聚),黑河为冷点(低-低集聚区),结合实际情况,对分析SO_2空间相关性来说,Moran指数相对G系数的分析结果更优。

Taking the atmospheric pollutant SO2 of the three northeastern provinces in 2017 as the research object,through global indicators(global Moran index,Geary coefficient),and regional indicators(Moran’I,local Geary’s C,local Getis’s G),the spatial aggregation of SO2 was analyzed and calculated.The detection results of the two indexes were compared.The results showed that in the analysis of spatial autocorrelation,the Moran index and Geary’s C index both indicated that there was significant spatial autocorrelation in SO2 in the three northeastern provinces;Moran scatter plot,LISA agglomeration map,and local G cluster agglomeration etc.all revealed the local spatial correlation of SO2 in prefecture-level cities in 36 prefecture-level cities in northeast China,That is,the low-value clusters(cold point)are mainly concentrated in the eastern part of the research area,and the high-value clusters(hot point)are concentrated in the southwest part of the research area;Through the analysis of the two indices,it could be found that in the southwestern part of the research area,Yingkou,Dalian and Tieling are low-high agglomeration areas in the Moran index,and Heihe is an unrelated area.However,in the local G coefficient,Yingkou,Dalian,Tieling are hot spots(high-high agglomeration)and Heihe is a cold spot(low-low agglomeration area).According to the actual situation,Moran index is better than G coefficient in analyzing the spatial correlation of SO2.