基于道路交叉点邻域扩张曲线的城市边界识别——以成都、西安、武汉、南京和长沙为例

城市边界识别是定性和定量研究城市的基础和前提,已有的关于城市边界提取的研究大都需要提前设定阈值或依赖人口统计数据。基于分形几何学,利用矢量建筑物分布数据识别城市边界,虽可克服这一缺陷,但国内城市边界的研究往往受阻于矢量建筑物分布数据获取困难。本文提出了一种基于道路交叉点的邻域扩张曲线作为识别城市边界的新方法。结果表明：该方法以电子地图为数据源,基于道路交叉点矢量数据进行研究时,城市集群数据随搜索半径增大而改变,城市扩张曲线中的最佳距离阈值是提取城市边界的关键;提取成都、西安、武汉、南京和长沙城市边界的最佳距离阈值分别为133、114、139、124和129 m,各城市的集群面积分别为769、350、270、317和359 km²。利用道路交叉点提取城市边界,方法简便可行,数据较易获得,本文结论有望为城市形态发展演变和城市规划等相关研究提供参考。

Identifying urban boundaries by clustering street node based on neighborhood dilation curve:A case study of Chengdu,Xi'an,Wuhan, Nanjing and Changsha

Identifying urban boundaries is the basis of qualitative and quantitative study of cities. Most of the existing studies on the identification of urban boundaries rely on predefined distance thresholds or incorporate census data. Although fractal geometry method using building vector maps to identify urban boundaries can overcome this problem, the research of urban boundary identification in China is often hindered by the difficulty of obtaining vector building distribution data. This study draws upon existing research results and puts forward a new method to identify urban boundaries by clustering street nodes based on neighborhood dilation curves. The results show that the key to this method that uses street nodes from electronic map as data source lies in finding the optimal distance threshold corresponding to the maximum curvature. The distance threshold for extracting urban boundaries of Chengdu, Xian, Wuhan, Nanjing, and Changsha are 133, 114, 139, 124, and 129 m; and the area of city clusters are 769, 350, 270, 317, and 359 km², respectively. The method of using street nodes vector data to identify urban boundaries is simple and feasible, and the data are easy to obtain. So the results of this study may provide some reference for the study of urban morphology, urban evolution, and urban planning.