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.

Key words: spatial statistics, spatial autocorrelation, global indicators, regional indicators, GIS, SO₂