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地理科学    2016, Vol. 36 Issue (8): 1134-1140    
  本期目录 | 过刊浏览 | 高级检索 |
新时期中国经济增长的空间格局
李在军1,2(),张雅倩1,2,胡美娟1,2,马志飞1,2,吴启焰1,2()
1.南京师范大学地理科学学院/江苏省地理信息资源开发与利用协同创新中心, 江苏 南京 210023
2.北部湾可持续发展监测与优化教育部重点实验室, 广西 南宁530001
Spatial Pattern of Chinese Economic Growth at County Level in the 21th Century
Zaijun Li1,2(),Yaqian Zhang1,2,Meijuan Hu1,2,Zhifei Ma1,2,Qiyan Wu1,2()
1.School of Geography Science, Nanjing Normal University, Jiangsu Center for Collaborative Innovation in Geograophical Information Resource Development and Application, Nanjing 210023, Jiangsu, China
2.Key Laboratory of Environment Change and Resources Use in Beibu Gulf (Ministry of Education), Nanning 530001, Guangxi, China
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摘要 

针对1998~2013年中国2 303个县域经济增长格局与变迁研究表明:县域经济空间关联格局相对较稳定,高高区呈团块式集中于东部沿海,带状式分布于内蒙地区,低低区散布中西部地区;县域增长时空关联特征及变化大体相一致,经济增长潜力或活力的环渤海、长江三角洲、珠江三角洲及内蒙古地区的县域单元LISA时间路径移动长度变化较大;中西部地区县域LISA时间路径长度较短,经济增长缺乏活力,而时间路径弯曲度最大的地区主要分布于缺乏稳定空间依赖方向的京九沿线、110°E附近及西藏部分县域。

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李在军
张雅倩
胡美娟
马志飞
吴启焰
关键词 县域经济空间格局时空动态性时空跃迁 
Abstract

Entering the 21th century, under the context of coordinated regional development strategy, identifying the trend of spatio-temporal evolution of regional economy growth plays an important role in timely optimizing spatial pattern and formulating reasonable spatial development strategy. By characterizing regional economy growth with per capita gross domestic product (GDP) and under the framework of exploratory spatio-temporal data analysis (ESTDA), this article focuses on analyzing spatio-temporal dynamic evolution of 2 303 counties’ economy growth in China in 1998-2013. The main results are following that: 1) There are synchronous trends of widening disparity and enhancing spatial association trends of county units’ economy growth. To be specific, regional economic growth disparity results from enhancing spatial association, while spatial association in space reflects widening regional economy growth disparity. 2) Local spatial association pattern of county units’ economic growth is relatively stable with time evolution. For instance, high-high areas like a mass type are mainly clustering in the eastern coastal region and exhibiting strip distribution type in Inner Mongolia region, and the amounts of high-high areas tend to increase. while low-low areas are mainly distributing in the vast Midwest region, but the amounts of low-low regions are decreasing. So, dominant economy core areas in future are still along both east-west axis of the Longhai-Lanxin showing belt development and along the north-south axis of eastern coastal regions. 3) The LISA time path can help find where are the most economic growth potential and vitality. The longest regions of LISA time path are mainly distributed around Bohai Sea, the Changjiang River Delta, the Zhujiang River Delta and the Inner Mongolia Region, while the shortest regions of LISA time path are widely located in Midwest counties which are economic backward areas and lack of upward economic momentum. Regions with maximum tortuosity of LISA time path are often lack of local stable spatial dependent direction, such as areas along the line of Beijing-kowloon railway, adjacent to longitude of 110°E and part of counties in Tibet. Hence, it is firstly essential to develop the Jing-Shan economic trough belt, then motivating its effect in connecting the west and east of China. 4) According to space-time transition matrix of local Moran's I, the maximum probability value of together up is just 0.061, while the frequency of type IV is 0.936 8. This implies that county units’ economic structure and local spatial association structure have higher stability and there exists to some degree path-dependent or space-locked mode.

Key wordscounty economic growth    characteristics of spatial pattern    space-time dynamics    space-time transitions
收稿日期: 2016-01-27      出版日期: 2016-10-26
基金资助:国家自然基金项目(41271176)资助
引用本文:   
李在军, 张雅倩, 胡美娟等 . 新时期中国经济增长的空间格局[J]. 地理科学, 2016, 36(8): 1134-1140.
Zaijun Li, Yaqian Zhang, Meijuan Hu et al . Spatial Pattern of Chinese Economic Growth at County Level in the 21th Century[J]. SCIENTIA GEOGRAPHICA SINICA, 2016, 36(8): 1134-1140.
链接本文:  
http://geoscien.neigae.ac.cn/CN/      或      http://geoscien.neigae.ac.cn/CN/Y2016/V36/I8/1134
Fig.1  县域经济的基尼系数及Moran’ s I
Fig.2  县域经济的LISA冷热区
Fig.3  县域经济LISA时间路径几何特征的空间分布
数量(次) HH LH LL HL
HH 7529 0.9386 0.0230 0.0050 0.0333
LH 3362 0.0455 0.8578 0.0840 0.0027
LL 20061 0.0016 0.0130 0.9684 0.0169
HL 3593 0.0610 0.0022 0.1063 0.8305
Table 1  Local Moran’s I时空跃迁矩阵
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