基于GIS的东北三省气温降水空间自相关性分析  

Spatial Autocorrelation Analysis of Temperature and Precipitation in Northeastern China Provinces Based on GIS

作　　者：郑兴文[1] 舒红[1] 许剑辉[1] 

机构地区：[1]武汉大学测绘遥感信息工程国家重点实验室,湖北武汉430079

出　　处：《地理空间信息》2014年第1期49-51,9,共3页Geospatial Information

基　　金：国家自然科学基金资助项目(41171313);国家863计划重点资助项目(2011AA010502);地理空间信息工程国家测绘地理信息局重点实验室开放研究基金资助项目(201329);民政部减灾和应急工程重点实验室开放基金资助项目(LDRERE20120203)

摘　　要：创新性地提出了基于两观测站点连线的垂直平分线构建空间连接矩阵,进一步计算空间自相关指数Moran＇s I 来分析气象数据的空间分布模式.利用Moran’s I 分析探讨了东北地区86 个气象站1951～2011 年气温降水数据的空间自相关.结果表明,东北三省气温降水存在显著的空间聚集现象,降水表现为吉林省北部显著低低聚集,吉林辽宁省东部显著高高聚集,气温表现为东北三省南北区域显著聚集;1960～2011 年气温分布较降水表现出更强的空间自相关性,且随年份变化自相关性稳定程度比降水好;气温分布空间自相关性随时间有减弱趋势, 但仍表现出区域显著聚集强相关现象.This paper innovatively proposed the method that build the spatial connection matrix based on the perpendicular bisector of two point connected line, and analyzed the cluster patter of meteorological data with Moran's I. The data of precipitation and temperature between 1951 and 2011 year from 86 weather stations in northeastern China was analyzed by the index of Moran's I. At the global scale, the temperature and precipitation of northeastern China Provinces have signii cant clustering patterns in space. At the region scale, precipitation has significant low-low clustering pattern in northern Jilin Province but significant high-high clustering pattern in eastern Liaoning and southeastern Jilin, meanwhile temperature exhibits significant clustering patterns in north and south regions of northeastern Provinces. Spatial autocorrelation of temperature is stronger than spatial autocorrelation of precipitation from 1960 to 2011, and the stationary of temperature spatial autocorrelation is more restrict. Spatial autocorrelation of temperature has a descending trend over time but still is significant.

关 键 词：东北三省 气温 降水 Moran'sI 空间自相关 

分 类 号：P208[天文地球—地图制图学与地理信息工程]