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中国区域发展时空格局变化分析及其预测
引用本文:贺三维,王伟武,曾晨,刘明辉.中国区域发展时空格局变化分析及其预测[J].地理科学,2016,36(11):1622-1628.
作者姓名:贺三维  王伟武  曾晨  刘明辉
作者单位:1. 中南财经政法大学公共管理学院,湖北 武汉 430073
2. 中南财经政法大学地方政府研究中心,湖北 武汉 430073
3. 浙江大学建筑工程学院,浙江 杭州 310058
4. 华中农业大学公共管理学院,湖北 武汉 430070
基金项目:国家自然科学基金项目(41601162、41571384)资助
摘    要:基于1997~2010年的全国省级、地级市和县级多尺度社会经济发展数据库,采用GIS与空间统计学相结合方法,揭示了中国经济发展的空间格局及其动态演变特征及时空格局的尺度依赖性,并对未来经济发展空间格局进行合理预测。结果表明: 1997~2010年中国经济的空间中心位于河南省境内,并呈现出西北移动的趋势,出现沿海-大陆共生的经济空间格局并逐步均衡化。 经济发展空间格局具有较为显著的尺度效应,其分布重心、形状和方位在不同尺度上发生变化;总体而言,空间尺度越小,其经济重心越偏向西南方向,其空间分布形状越接近于正圆,主轴方位越偏东。预测结果表明未来10~20 a中国经济重心会继续向北移动,略微偏向东部地区,京津冀、长三角等沿海城市群仍是未来中国经济发展的主要增长引擎。研究结果可以为各级政府制定区域发展政策提供科学的理论依据。

关 键 词:经济发展  空间格局  多尺度  标准差椭圆  灰色预测模型  
收稿时间:2016-02-23
修稿时间:2016-08-13

Spatio-temporal Pattern of Economic Development and the Forecast in China
Sanwei He,Weiwu Wang,Chen Zeng,Minghui Liu.Spatio-temporal Pattern of Economic Development and the Forecast in China[J].Scientia Geographica Sinica,2016,36(11):1622-1628.
Authors:Sanwei He  Weiwu Wang  Chen Zeng  Minghui Liu
Institution:1. School of Public Administration, Zhongnan University of Economics and Law, Wuhan 430073, Hubei, China
2. Research Center of local government, Zhongnan University of Economics and Law, Wuhan 430073, Hubei, China
3. College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, Zhejiang, China
4. School of Public Administration, Huazhong Agricultural University, Wuhan 430070, Hubei, China
Abstract:The issue of regional inequality has attracted much attention of geographers and economists. The interdisciplinary research has been reflected in a spatio-temporal hierarchical structure, that is, the multi-scalar nature of economic development and its dynamics. With the advent of the new economic geography, the role of space is emphasized in the field of regional science. Spatial pattern and its dynamics provide a significant prospective for understanding regional development in China. Spatial pattern statistics has been adopted to explore the multi-scalar spatial patterns of regional development in China and to investigate the corresponding scaling effect. Especially the forecast of spatial pattern of economic development allows the government to make effective policies. First, the standard ellipse is employed to explore the multi-scalar globe spatial pattern and its dynamics so as to investigate the scaling effect. Second, grey forecast model and Geographic Information System are utilized to predict and visualize the future spatial pattern in 2020 and 2030. The study area is composed of 2 254 county-level units including city districts/counties/county-level cities (not including Hongkong, Macao and Taiwan of China), 333 prefecture-level units and 31 provincial-level units. Major results are listed as follows: 1) Economic gravity in China is situated in Henan Province from 1997 to 2010, accompanied by a shift towards northwestern China, demonstrating co-developing spatial pattern of the coastal and inland regions. It contributes to the effective implementation of western development and rise of central China. Foreign direct investment starts to leave the Zhujiang River Delta and concentrate in Bohai Rim and the Changjiang River Delta. 2) 66% of economic development mainly happens in most eastern and central parts as well as one third of western region. Spatial pattern of economic development shows obvious scaling effect in economic gravity, shape and direction. Overall, the finer the spatial scale, the more southwestern-oriented economic gravity is. Its shape of standard ellipse tends to be circled and its direction is by east. 3) It is forecasted that economic gravity will continue to shift northward and slightly towards the east, indicating Beijing-Tianjin-Hebei and the Changjiang River Delta as the major growth engine for economic development in the future. 4) It has been demonstrated that spatial pattern of regional development has obvious scaling effect. The county level is better to understand the finer spatial structure and spatial laws.
Keywords:economic development  spatial pattern  multi-scalar  standard ellipse  grey forecast model  
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