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当前位置: 首页 > 文章 > 大气污染物SO_2空间相关性的空间集聚分析 湖北农业科学 2019 (8) 56-60
Position: Home > Articles > Spatial agglomeration analysis of spatial correlation of atmospheric pollutant SO_2 Hubei Agricultural Sciences 2019 (8) 56-60

大气污染物SO_2空间相关性的空间集聚分析

作  者：
刘梅;张冬有
单  位：
黑龙江省普通高等学校地理环境遥感监测重点实验室/哈尔滨师范大学地理科学学院
关键词：
空间统计;空间自相关;全局指标;区域指标;GIS;SO_2
摘  要：
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。
译  名：
Spatial agglomeration analysis of spatial correlation of atmospheric pollutant SO_2
作  者：
LIU Mei;ZHANG Dong-you;Key Laboratory of Remote Sensing Monitoring of Geographic Environment,College of Heilongjiang Province/College of Geographical Science,Harbin Normal University;
关键词：
spatial statistics;;spatial autocorrelation;;global indicators;;regional indicators;;GIS;;SO_2
摘  要：
Taking the atmospheric pollutant SO_2 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 SO_2 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 SO_2 in the three northeastern provinces; Moran scatter plot, LISA agglomeration map,and local G cluster agglomeration etc. all revealed the local spatial correlation of SO_2 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 SO_2.

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