地理科学进展 ›› 2017, Vol. 36 ›› Issue (6): 667-676.doi: 10.18306/dlkxjz.2017.06.002

• 研究论文 • 上一篇    下一篇

基于网络分析的城市影响区和城市群空间范围识别

潘竟虎*(), 戴维丽   

  1. 西北师范大学 地理与环境科学学院,兰州 730070
  • 出版日期:2017-06-20 发布日期:2017-06-20
  • 通讯作者: 潘竟虎 E-mail:panjh_nwnu@nwnu.edu.cn
  • 作者简介:

    作者简介:潘竟虎,博士,教授,研究方向为空间经济分析,E-mail:panjh_nwnu@nwnu.edu.cn

  • 基金资助:
    国家自然科学基金项目(41361040,41661025);西北师范大学青年教师科研能力提升计划项目(NWNU-LKQN-16)

Identification of urban hinterlands and urban agglomerations in China based on network analysis

Jinghu PAN*(), Weili DAI   

  1. College of Geographic and Environmental Science, Northwest Normal University, Lanzhou 730070, China
  • Online:2017-06-20 Published:2017-06-20
  • Contact: Jinghu PAN E-mail:panjh_nwnu@nwnu.edu.cn
  • Supported by:
    National Natural Science Foundation of China, No.41361040, No.41661025;Research Capacity Promotion Program for Young Teachers of Northwest Normal University,No.NWNU-LKQN-16

摘要:

以不打破行政界线为前提,利用Huff模型,基于矢量数据,采用最短交通路网距离和城市综合规模值来综合测算腹地与中心城市之间的势能,根据势能确定腹地县域的归属,对全国地级及以上城市的空间影响范围进行识别,并和基于改进场强模型的城市影响腹地范围界定结果进行对比。以全国发育较成熟的15个城市群为对象,综合测定城市群的影响范围,并与城市群规划中的空间范围进行对比,提出城市群空间范围调整建议。并以长株潭城市群为例,通过社会网络分析方法,综合分析测定城市群的空间影响范围。研究结果可为城市(群)规划提供科学依据和方法参考。

关键词: 空间影响范围, 城市群, 网络分析, Huff模型, 城市腹地

Abstract:

With the rapid expansion of city scale, the competition between central cities and their surrounding cities is increasing and therefore accurate identification of urban hinterland area is beneficial for the assessment of urban development strength and potential. Delineating the maximum limit of the hinterland area of cities has become an important research question, whose result can provide support for cities to formulate developing strategies and improve their comprehensive strength, and to achieve the joint development of cities and hinterlands. This study first used weighted average travel time as an indicator to measure the national spatial accessibility for China in 1991, 2010, and 2020. Second, we selected 30 provincial administrative regions as the research object and used the Huff model to identify the influencing hinterlands of cities at the prefecture level and above, without breaking the administrative boundaries. The results indicate that the shortest weighted average travel time decreased from 14.37 h in 1991 to 5.12 h in 2010. The weighted average travel time will further decrease from 5.12 h in 2010 to 4.39 h in 2020 due to the large-scale operation of the high-speed railways. For the same reason, interurban spatial accessibility has notably improved. The Huff model comprehensively measures potential energy between hinterland cities and central cities using the shortest traffic road-network distance and urban synthesized scale value, and selects maximum value of potential energy to determine the membership of hinterland cities. The advantage of this model is to maintain the existing administrative boundaries, which is convenient for practical planning. The shortest interurban time distance matrix was calculated for 15 national mature-growth urban agglomerations by using spatial network analyst in order to evaluate interurban economic linkage intensity. The economical linkage intensity between every district was accumulated to reflect economic function intensity in an area; and urban agglomerations were measured according to the urban comprehensive scale value, commute time, economic linkage intensity of urban agglomeration and total GDP of districts. The result was compared with planned urban agglomeration scales, thus putting forward suggestions for detailed adjustments for urban agglomerations. This study selected representative urban agglomerations of the Changsha-Zhuzhou-Xiangtan area as a case study and by using the social network analyst method, such as network density analyst, centrality, and core-edge construction, the spatial influence scale of urban agglomerations were comprehensively measured. The results can provide a scientific basis and methodological reference for urban agglomeration planning.

Key words: spatial influencing area, urban agglomeration, network analysis, Huff model, urban hinterland