基于空间句法理论的城市群核心区发展边界识别

doi:10.11821/dlyj020190516

地理研究 ›› 2020, Vol. 39 ›› Issue (6): 1418-1426.doi: 10.11821/dlyj020190516

基于空间句法理论的城市群核心区发展边界识别

武凯华^1,², 李朝奎^1,²(), 刘俊杰^1,², 郝豪杰^1,²

^1. 湖南科技大学地理空间信息技术国家地方联合工程实验室,湘潭 411201
^2. 湖南科技大学资源环境与安全工程学院,湘潭 411201

收稿日期:2019-06-24 修回日期:2019-11-26 出版日期:2020-06-20 发布日期:2020-08-20
通讯作者: 李朝奎
作者简介:武凯华（1996-）,男,新疆北屯人,硕士研究生,主要从事城市空间分析与建模研究。E-mail: 2481590173@qq.com
基金资助:
国家重点研发计划(2017YFB0503802);国家重点研发计划(2018YFB0505401);国家自然科学基金项目(41271390);湖南省研究生科研创新项目(CX20190798)

Identification of growth boundary of core areas in an urban agglomeration based on spatial syntax theory

WU Kaihua^1,², LI Chaokui^1,²(), LIU Junjie^1,², HAO Haojie^1,²

^1. National and Local Joint Engineering Laboratory of Geospatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, Hunan, China
^2. College of Resources, Environment and Safety Engineering, Hunan University of Science and Technology, Xiangtan 411201, Hunan, China

Received:2019-06-24 Revised:2019-11-26 Online:2020-06-20 Published:2020-08-20
Contact: LI Chaokui

摘要/Abstract

摘要：

准确识别当前城市群建设进程中核心区发展边界是研究城市群的一项重要内容。本文提出一种研究思路：采用空间句法分析城市群道路网,将得到的4个分析指标融合成新指标——“城市群集群度”,并提取“城市群集群度”等值线和“城市群集群度”曲线,通过计算找到最佳阈值从而提取出城市群核心区发展边界。以长株潭城市群为例,将基于空间句法的研究结果与基于Densi-Graph方法的研究结果进行对比,在除去数据质量因素后,城市群核心区发展边界识别差异有望控制在10%以内。研究表明：基于空间句法理论的城市群核心区发展边界识别方法容易获取计算数据,适用范围广,可靠性强。

关键词: 城市群, 边界提取, 空间句法理论, 长株潭城市群

Abstract:

Accurately identifying the boundary of the core areas in the process of urban agglomeration construction is an important part of urban agglomeration research. In this paper, a method of extracting urban agglomeration growth boundary is proposed: using spatial syntax to analyze urban agglomeration road network, fusing the four analysis indicators into a new index "urban agglomeration cluster degree", and putting forward the "urban agglomeration cluster degree" contour and "urban agglomeration cluster degree" curve, finding the best threshold through mathematical calculation, thus extracting the urban agglomeration core area growth boundary. This paper takes the Changsha-Zhuzhou-Xiangtan urban agglomeration as an example. The research results of this method are compared with those of the classical method. After the data quality factor is removed, the growth boundary recognition error of the core areas of urban agglomeration is expected to be controlled within 10%. Experiments show that the urban agglomeration core area growth boundary recognition method based on spatial syntax theory is easy to obtain research data, and has a wide application range and high reliability. The research content and methods proposed in this paper can provide a new idea for urban and urban agglomeration research.

Key words: urban agglomeration, boundary extraction, spatial syntax theory, Chang-Zhu-Tan urban agglomeration

武凯华, 李朝奎, 刘俊杰, 郝豪杰. 基于空间句法理论的城市群核心区发展边界识别[J]. 地理研究, 2020, 39(6): 1418-1426.

WU Kaihua, LI Chaokui, LIU Junjie, HAO Haojie. Identification of growth boundary of core areas in an urban agglomeration based on spatial syntax theory[J]. GEOGRAPHICAL RESEARCH, 2020, 39(6): 1418-1426.

图/表 4

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基于空间句法理论的城市群核心区发展边界识别

Identification of growth boundary of core areas in an urban agglomeration based on spatial syntax theory

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