【摘要】 以东北三省2017年的大气污染物SO₂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SO₂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SO₂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SO₂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SO₂空间相关性来说,Moran指数相对G系数的分析结果更优。更多还原

【Abstract】 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.更多还原