School of Management, Nanchang University, Nanchang 330031, China.
School of Architecture, Tsinghua University, Beijing 100084, China.
Int J Environ Res Public Health. 2022 Apr 23;19(9):5153. doi: 10.3390/ijerph19095153.
As urban spatial patterns are the prerequisite and foundation of urban planning, spatial pattern research will enable its improvement. The formation mechanism and definition of an urban "production-living-ecological" space is used here to construct a classification system for POI (points of interests) data, crawl POI data in Python, and DBSCAN (density-based spatial clustering of application with noise) to perform cluster analysis. This mechanism helps to determine the cluster density and to study the overall and component spatial patterns of the "production-living-ecological" space in the central urban area of Wuhan. The research results are as follows. (1) The spatial patterns of "production-living-ecological" space have significant spatial hierarchical characteristics. Among them, the spatial polarizations of "living" and "production" are significant, while the "ecological" spatial distribution is more balanced. (2) The "living" space and "production" space noise points account for a small proportion of the total and are locally clustered to easily become areas with development potential. The "ecological" space noise points account for a large proportion of the total. (3) The traffic accessibility has an important influence on the spatial patterns of "production-living-ecological" space. (4) The important spatial nodes of each element are consistent with the overall plan of Wuhan, but the distribution of the nodes for some elements is inconsistent. The research results show that the POI big data can accurately reveal the characteristics of urban spatial patterns, which is scientific and practical and provides a useful reference for the sustainable development of territorial and spatial planning.
由于城市空间格局是城市规划的前提和基础，因此空间格局研究将促进城市规划的发展。本研究采用城市“生产-生活-生态”空间的形成机制和定义构建了 POI（兴趣点）数据分类系统，使用 Python 爬虫技术获取 POI 数据，并采用 DBSCAN（基于密度的空间聚类算法）进行聚类分析。该机制有助于确定聚类密度，并研究武汉市中心城区“生产-生活-生态”空间的整体和组成部分的空间格局。研究结果表明：（1）“生产-生活-生态”空间格局具有显著的空间层次特征。其中，“生活”和“生产”空间极化明显，而“生态”空间分布更为均衡。（2）“生活”空间和“生产”空间的噪声点占比较小，呈局部聚集，容易成为具有发展潜力的区域；“生态”空间的噪声点占比较大。（3）交通可达性对“生产-生活-生态”空间格局有重要影响。（4）各要素的重要空间节点与武汉市的总体规划一致，但部分要素的节点分布不一致。研究结果表明，POI 大数据能够准确揭示城市空间格局的特征，具有科学性和实用性，为国土空间规划的可持续发展提供了有益参考。
