摘要
以杭州市西湖区为实证,采用高德地图POI数据,通过DBSCAN聚类识别459个社区中心,从空间布局、组织形式、空间关联3方面入手刻画其空间特征,在此基础上通过Logistic回归判断中心数量、能级的影响因素,研究表明:①社区中心分布呈现空间集聚性和“一区多片”的整体结构,具有圈层分布和道路亲缘性特征。②社区中心存在“强−弱”的中心网络结构,在小尺度上则由强弱中心互补形成团簇,并在社区层面形成功能较为完整的网络结构。③社区中心内公共服务有引领性作用,能促进社区中心形成,且中心内设施具有同类集聚的特征。④常住人口总量是最主要的影响要素,道路分割、户籍比、平均高程、社区组织类型对中心的形成有显著意义,人口密度、公司企业数等对中心数量与能级有重要影响。
The study takes the Xihu District of Hangzhou as an example,and uses the Amap POI data to identify 459 community centers through DBSCAN clustering,and describe its spatial characteristics from 3 parts:spatial layout,organizational form,and spatial association.Then explore the factors of the number and energy level of the center by logistic regression.the main conclusions are:1)The spatial distribution of community centers has agglomeration characteristic and a spatial structure with‘multi-pieces and one group’.Community centers has circle-like distribution pattern and road affinity.2)There is a“strong-weak”network structure in the distribution of community centers.On a smaller scale,clusters are formed by the strong and weak centers complement each other,and a more complete network structure is formed at the community level.3)The public services inside the community center have a guiding role,which can promote the formation of the community center,and the facilities inside the center have similar agglomeration characteristics.4)The total number of permanent residents is the main influence factor.Road division,household registration ratio,average elevation,and type of community organization are significant for the formation of the center.Population density and the number of companies have important influence on the number and energy levels of community centers.
作者
阮一晨
刘声
李王鸣
章明宇
Ruan Yichen;Liu Sheng;Li Wangming;Zhang Mingyu(College of Civil Engineering and Architecture,Zhejiang University,Hangzhou 310058,Zhejiang,China;City College,Zhejiang University,Hangzhou 310015,Zhejiang,China)
出处
《地理科学》
CSSCI
CSCD
北大核心
2021年第1期74-82,共9页
Scientia Geographica Sinica
基金
国家自然科学基金项目(51908495)资助。
关键词
社区中心
密度聚类
关联挖掘
杭州西湖区
community center
DBSCAN
Apriori
the Xihu District in Hangzhou