地理科学进展 ›› 2018, Vol. 37 ›› Issue (9): 1257-1267.doi: 10.18306/dlkxjz.2018.09.008

• 研究论文 • 上一篇    下一篇

多重城市网络空间结构及影响因素——基于有向多值关系视角

宋琼1(), 赵新正1, 李同昇1,*(), 刘静玉2   

  1. 1. 西北大学城市与环境学院,西安 710127
    2. 河南大学环境与规划学院,河南 开封 475004
  • 收稿日期:2017-12-14 修回日期:2018-05-03 出版日期:2018-09-28 发布日期:2018-09-28
  • 通讯作者: 李同昇 E-mail:songqiongnwu@163.com;leetang@nwu.edu.cn
  • 作者简介:

    作者简介:宋琼(1990-),女,河南林州人,博士研究生,主要从事经济地理与区域发展研究,E-mail: songqiongnwu@163.com

  • 基金资助:
    国家自然科学基金项目(41401184);教育部人文社会科学基金项目(14YJCZH222);陕西省教育厅科学研究计划项目(14JK1753);西北大学研究生自主创新项目(YZZ17148) [Foundation: National Natural Science Foundation of China, No.41401184

Spatial structures and influencing factors of multiple urban networks based on the perspective of directed-multivalued relation

Qiong SONG1(), Xinzheng ZHAO1, Tongsheng LI1,*(), Jingyu LIU2   

  1. 1. College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China
    2. College of Environment and Planning, Henan University, Kaifeng 475004, Henan, China
  • Received:2017-12-14 Revised:2018-05-03 Online:2018-09-28 Published:2018-09-28
  • Contact: Tongsheng LI E-mail:songqiongnwu@163.com;leetang@nwu.edu.cn
  • Supported by:
    Project of Humanities and Social Sciences of Ministry of Education, China, No.14YJCZH222;Natural Science Foundation of MOE in Shaanxi Province, No.14JK1753;Graduate Student Innovation Project of Northwest University, No.YZZ17148

摘要:

从有向多值关系视角,以中原城市群为案例区,对比分析静态网络、百度信息网络、综合交通网络的空间结构,选择经济、文化、行政、时间距离方面的7个因素构建关系回归模型,剖析3个网络的影响因素。结果表明:①3个网络之间的相关系数表现为:百度信息网络与综合交通网络>百度信息网络与静态网络>综合交通网络与静态网络且其值均高于0.582,表明整体的相似性大于差异性;②在空间结构上,3个网络均表现出以郑州为中心,以京广线和陇海线为发展轴,郑州、开封、新乡、洛阳、许昌构成了骨干网络,并形成辐射与集聚优势共存的中心片区;在中心片区之外,3个网络表现出明显的差异性特征。网络节点的辐射与集聚能力在百度信息与综合交通网络中均是正相关,而在静态网络中则是负相关;③关系回归模型的7个因素均对3个网络产生了不同程度的影响,其中企业合作、行业结构相似、经济制度邻近是影响3个网络相似性较强的因素,而收入差距、文化相似、行政隶属、平均时间距离是影响它们之间差异性的因素。本研究为关系转向下的城市体系研究提供了新的思路。

关键词: 多重城市网络, 空间结构, 关系回归模型, 影响因素, 有向多值关系, 中原城市群

Abstract:

Urban system is one of the core subjects of human-economic geography. From the perspective of directed-multivalued relation, taking the Zhongyuan urban agglomeration as the case study area, a comparative study on spatial structures and influencing factors of multiple urban networks—static network, Baidu information network, and comprehensive transportation network—will provide a new train of thought for urban system research under relational transition. This study analyzed the hierarchical structure and the node structure of the networks of the Zhongyuan urban agglomeration, to extract generality and particularity of the three networks mentioned above. Furthermore, we built a relational regression model with the help of the quadratic assignment procedure (QAP) method to dissect the influencing factors of these three networks, from the aspects of economy, culture, administration, and time distance. The empirical results are as follows: (1) The correlation coefficients of the three networks are over 0.582, presented as Baidu information versus comprehensive transportation > Baidu information versus static > Comprehensive transportation versus static, and the overall similarity is stronger than the difference. (2) Integrating the three networks, the Zhongyuan urban agglomeration, a mononuclear spatial structure cored by Zhengzhou, consisted of two regional development belts along with the Beijing-Guangzhou railway and the Lianyungang-Lanzhou railway, and formed a central area with obvious advantages of radiation and concentration with a backbone network constituted by Zhengzhou, Kaifeng, Xinxiang, Luoyang, and Xuchang. Outside of the central area, the three networks showed obvious particularity. The radiation and agglomeration ability of cities have negative correlation in static network, while showed positive correlation in Baidu information network and comprehensive transportation network. (3) The seven influencing factors have different effects on the three networks. Enterprise cooperation, industrial structure similarity, and economic system proximity lead to the similarity of the three networks. Meanwhile, income gap, cultural similarity, administrative affiliation, and average time distance lead to their differences.

Key words: multiple urban networks, spatial structure, relational regression model, influencing factors, directed-multivalued relation, Zhongyuan urban agglomeration