长江流域资源与环境 >> 2017, Vol. 26 >> Issue (09): 1295-.doi: 10.11870/cjlyzyyhj201709001

• 区域可持续发展 •    下一篇

长江经济带土地城镇化时空格局及其驱动力研究

张立新,朱道林,杜挺,谢保鹏,郭稷桁   

  1. (1.中国农业大学资源与环境学院,北京 100193;2.中国土地政策与法律研究中心,北京100193)
  • 出版日期:2017-09-20

SPATIOTEMPORAL PATTERN EVOLVEMENT AND DRIVING FACTORS OF LAND URBANIZATION IN YANGTZE RIVER ECONOMIC BELT

ZHANG Li-xin1,ZHU Dao-lin1,2, DU Ting1, XIE Bao-peng1,GUO Ji-heng   

  1. (1.College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China2.Center for Land Policy and Law, Beijing 100193, China)

  • Online:2017-09-20

摘要: 研究长江经济带土地城镇化时空格局演变及驱动因素,对长江经济带新型城镇化建设具有重要的理论及实践意义。测度了1994~2014年长江经济带121个地级市土地城镇化水平,并利用探索性空间数据分析方法(ESDA)探究其时空格局演变特征,最后运用偏最小二乘回归分析模型(PLS)探讨了土地城镇化空间格局差异形成的主要驱动因素。研究结果表明:时序演变上,长江经济带土地城镇化水平总体上呈现不断上升的趋势,并表现出阶段性变化特征;空间演化上,长江经济带土地城镇化水平空间差异明显,主要表现为:空间自关联性显著,且土地城镇化热点区域作为极化的核心,集聚趋势由东北向西南方向逐渐递减;长江经济带土地城镇化水平时空差异受到经济发展水平、产业结构升级、人口规模、固定资产投资和区位条件等因素的共同作用影响。

Abstract:

Making a study of the spatial and temporal pattern evolution and driving factors of land urbanization in Yangtze River Economic Belt, has the important theoretical and practical significance for new-type urbanization construction. This study measured the land urbanization level of 121 Prefecture-level cities in Yangtze River Economic Belt from1994 to 2014.By using the Exploratory Spatial Data AnalysisESDAto explore the spatio-temporal characteristics of the land urbanization. Discussing the driving factors with the method of Partial Least Squares RegressionPLS.The results of the study showed that: Land urbanization level in Yangtze River Economic Belt revealed a growing trend as welland showed the characteristics of periodic variation. The spatial differences of land urbanization in Yangtze River Economic Belt were mainly represented that as the core of the polarization, agglomeration trend of the land urbanization hot zones had taken on gradually decreasing from northeast to southwest. The land urbanization level in Yangtze River Economic Belt was affected by various factors, for example: economic performance, industrial structure, population scale, fixed assets investment, and regional factors.

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