中文摘要：

以青海省43个县（市、区）及3个行政委员会和一个乡（格尔木市的飞地）为基本空间单元，以2006年青海省县域人均GDP等经济数据及人口数据为属性数据，在ArcView、ARCGIS等GIS软件和GeoDA空间统计分析软件的支持下，利用空间自相关分析理论，对青海省县域经济之间空间差异进行了实证分析。表明：（1）青海省2006年经济重心与人口重心及几何中心均有较大偏移，GDP及人均GDP重心也有较大偏移；（2）总体上青海省2006年经济空间差异较大，Moran’sI指数为0．5138，空间自相关特性明显，经济发展水平相似的地区在空间上集中分布；（3）目前，青海省城市与区域相互作用类型属离散型和聚集型，而扩散型及均衡型尚未出现。青南广大地区属离散型，海西地区主要是由资源要素禀赋导致集聚型，海东地区特别是西宁市由于较好的自然条件、优越的空间区位、便利的交通及较完善的基础设施而产生较强的集聚效应，海东其它广大地区Moran’sI指数均较低（包括西宁市的两个市辖县），充分说明海东地区城市的扩散作用尚未显现，特别是省会城市西宁市对周围县域经济的拉动作用也不明显。

英文摘要：

Region is an open system. The economic growth in each region not only depends on its own characteristics, but also on those of the regions that form the neighborhood to which it belongs. Growth poles theory or coreperiphery pattern suggests that different spatial interactions between a region and its neighbors can enlarge or lessen regional disparities. Unfortunately, the traditional approaches to regional disparities suppose that each region is independent from the others, so they just evaluate the regional disparity variations of ＂isolated＂ regions and cannot tell the dynamics of interrelated regions. This paper firstly explores the feasibility of exploratory spatial data analysis （ESDA） in analyzing regional economic disparities. Unlike traditional statistical methods, such as coefficients of variation, ESDA can incorporate spatial effects into the analysis of regional disparities, identifying global and local disparities at the regional scale and visualizing the spatial distribution and patterns through Moran scatterplots or maps. By means of global and local spatial autocorrelation analyses, this paper then investigates the spatial dynamics of regional disparities at the county level in Qinghai Province by analyzing per capita GDP data, 2006, with the support of ArcView GIS and GeoDA software. The conclusions are drawn as follows： （ 1 ） Empirical results show that the center of population and GDP have a significant offset with the center of geometry. （2） The overall county level spatial disparities of regional economy in Qinghai province is great in 2006, and the global Moran＇ s I is 0. 513 8, which indicates that there are significant positive spatial autocorrelation of per capita GDP in Qinghai Province. That is, the relatively high （low） developed county tends to be located nearby other high （low）developed counties more often than expected due to random chance. Local Moran＇ s I scatter plots and LISA （ Local Indicators of Spatial Association） cluster map have been used to