地理学报 ›› 2015, Vol. 70 ›› Issue (4): 528-538.doi: 10.11821/dlxb201504002

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基于边际K函数的长三角地区城市群经济空间划分

葛莹1(), 蒲英霞3, 赵慧慧1, 李云婷1   

  1. 1. 河海大学地球科学与工程学院,南京 210098
    2. 多伦多大学地理系,加拿大 多伦多 M1C1A4
    3. 南京大学地理与海洋科学学院,南京 210023
  • 收稿日期:2014-08-25 修回日期:2015-01-29 出版日期:2015-04-20 发布日期:2015-06-11
  • 作者简介:

    作者简介:葛莹(1963-), 女, 浙江省慈溪县人, 博士, 教授, 中国地理学会会员(S110009069M), 主要从事产业集聚、空间统计学及GIS等研究。E-mail: geying@hhu.edu.cn

  • 基金资助:
    国家自然科学基金项目(41071347)

Dividing economic space into urban agglomerations usingthe marginal K function:A case study of the Yangtze River Delta region

Ying GE1(), MIRON John2, Yingxia PU3, Huihui ZHAO1, Yunting LI1   

  1. 1. School of Earth Science and Engineering, Hohai University, Nanjing 210097, China
    2. Department of Human Geography, University of Toronto Scarborough, Toronto, Ontario M1C 1A4, Canada
    3. School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China
  • Received:2014-08-25 Revised:2015-01-29 Online:2015-04-20 Published:2015-06-11
  • Supported by:
    National Natural Science Foundation of China, No.41071347

摘要:

基于空间点模式分析的Ripley's K函数,结合微观经济学的边际分析法,通过集聚度和边际集聚两个指标,从理论上探索城市群集聚效应的定量测度方法,提出一种城市群的经济空间划分方法。本文的不同在于,多尺度估算城市的集聚度和边际集聚,以城市的边际集聚极值点时的城市区域布局模式为最优,据此划分城市群的经济空间,并以长江三角洲地区为例,对本文提出的方法进行实证。研究结果表明:① 城市的集聚度估计显示,长三角地区2010年城市空间布局为随机分布型,但随着观测尺度的增加,城市的集聚度呈快速上升趋势。② 边际集聚估计揭示,当城市区位和城市规模的集聚尺度分别为173 km和185 km时,城市区位或规模的集聚效应达到峰值,此时长三角地区城市空间布局出现最优模式。③ 空间聚类分析展示,在城市区域布局的最优空间模式下,长三角地区呈现“中心—外围”的经济空间结构,高集聚度子群是区域经济发展中心,全部位于上海经济辐射圈,而低集聚度子群是外围欠发达地区,全部位于区际行政边界,暗示边际负效应仍阻碍着地区内外人员的往来。

关键词: 经济空间划分, Ripley's K函数, 边际分析法, 城市群, 长江三角洲地区

Abstract:

In this study, an economic space-dividing method for urban agglomerations is presented to theoretically explore the quantitative measurement of urban agglomeration clustering effects with two parameters (agglomeration degree and marginal agglomeration). A marginal analysis of microeconomics based on Ripley's K function of spatial point pattern analysis is also conducted. The study is novel in the aspect that economic space is divided via urban agglomeration degree and marginal agglomeration multi-scale estimation, and an optimal urban pattern is identified when marginal urban agglomeration reaches its maximum value. Finally, urban agglomeration economic spaces are determined accordingly. The Yangtze River Delta is taken as a case study to validate the proposed method. The results show that: (1) urban agglomeration degree estimates indicate that the urban spatial pattern of the Yangtze River Delta region in 2010 was random, but that of the region has shown a rapid increasing trend with the increase of scales of observation; (2) estimates of marginal agglomeration indicate that clustering effects of urban location and urban size reach peak values when city location and city size agglomeration scales reach 185 km to 173 km, respectively. At this point, the urban spatial pattern of the region achieves an optimized state; (3) results of spatial clustering analysis show that in the optimal spatial urban pattern, the Yangtze River Delta region exhibits a "core-periphery" spatial economic structure, in which highly clustered sub-agglomerations located in the Shanghai economic radiation circle form regional centers of economic development, while poorly clustered sub-agglomerations located along inter-regional administrative borders remain underdeveloped peripheral areas. This suggests that a negative marginal effect still hampers the migration of individuals across such areas.

Key words: economic space division, Ripley's K function, marginal analysis, urban agglomeration, Yangtze River Delta