基于空间句法理论的城市群核心区发展边界识别

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。

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

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.