摘要
以沈阳经济区23个县为研究单元,基于BP神经网络和ESDA方法,选取人均GDP、人均财政收入等9项指标建立评价体系,分别研究2005和2010年县域经济空间分异的特征和驱动因素,并得出结论如下:①2005年沈阳经济区县域经济南北差异显著,2010年南北差异弱化。2005—2010年,南部的海城、大石桥等县市的经济水平较高,东北部的开原、调兵山等县市的经济水平上升,中南部的辽阳、灯塔等县市经济水平下降。②2005—2010年,沈阳经济区经济水平相近的县市在空间上的集聚特征有所弱化;经济发展水平较高的县市在空间分布上形成沿交通轴线布局的县域经济带。③从局部差异来看,2005年在沈阳经济区南部形成岫岩、桓仁等"高高"型县域集聚区,以及北部的彰武、康平等"低低"型县域集聚区。到2010年,除开原市属于"高低"型县域外,其他县域经济的空间极化特征不明显。④深入分析沈阳经济区县域经济空间分异的驱动因素,得出结论如下:区位条件是县域经济空间分异的重要因素,自上而下的政策驱动是县域经济空间分异的外部动力,中心城市的极化扩散作用是县域经济空间分异的重要力量。
On the basis of the BP neural network and ESDA technology, this paper uses 9 economic indicators to analyses the spatial differentiation of county-level economy in the Shenyang Metropolitan Area in 2005 and 2010. The results are as follows. Firstly, there is a narrowing tendency of the spatial differentiation of county-level economy between the southern and northern Liaoning province in 2005 and 2010. The economic evaluation values of some industrialized counties such as Haicheng and Dashiqiao are high from 2005 to 2010, the values of the central-south counties such as Dengta and Liaoyang decline, and the northeast counties such as Kaiyuan and Diaobingshan increase at the same time. Secondly, the decline of Moran＇ s I from 2005 to 2010 shows that counties of similar economic development levels are spatially scattered, while counties of better economic levels distribute along the main transportation axes. Thirdly, in terms of local differences, in 2005, Xiuyan and Huanren belong to ＂H-H＂ type, and Zhangwu and Kangping are ＂L-L＂ type counties. In 2010, Kaiyuan is ＂H-L＂ type, while the other counties don＇ t show the extremely spatial distribution characteristics, which means that the counties of different economic levels arc of a random spatial distribution pattern. At last, the paper holds the view point that the location condition is the basic factor for the formation of the spatial differentiation of county-level economy, the top-down regional development policies perform as external impetus, and the polarization and diffusion influence of regional central cities are the important force.
出处
《经济地理》
CSSCI
北大核心
2012年第12期79-84,共6页
Economic Geography
基金
国家自然科学基金项目(41071108)
国家科技支撑计划课题(2008BAH31B06)
国家自然科学青年基金项目(41001076)
中国科学院知识创新工程重要方向项目(KZCX2-YW-342)
关键词
县域经济差异
BP神经网络
ESDA技术
沈阳经济区
county-level economic differentiation
central cities are the important force BP neural network
ESDA
Shenyang Metropolitan Area
