摘要
通过高德地图API获取的贵阳市中心城区科教文化设施点数据,验证DBSCAN算法在识别城市科教文化设施集群识别中的应用,研究共识别有效POI2 673个,科教文化设施集群163个,集群在空间特征上表现出以贵阳市老城区为核心的中心发散型结构。集群规模共划分为4个等级,通过计算各等级结构的空间形态指标,分析贵阳市科教文化设施集聚特征规律。研究发现,科教文化设施资源配置不均衡,高等级集群分布差异明显,难以实现科教文化资源共享。研究利用DBSCAN算法识别科教文化设施集群,为深入挖掘城市地理POI信息提供理论方法,同时为定量认知城市实体空间规划、优化城市资源配置提供支撑。
Based on the data of science and education cultural facilities in the downtown area of Guiyang obtained through Amap API, the application of DBSCAN algorithm in identifying clusters of urban science and education cultural facilities has been verified. 2673 effective POIs and 163 science and education cultural facilities clusters were identified, and a central divergent structure of the clusters has been showed centered on the old city town of Guiyang. The clusters were divided into four levels, by calculating the spatial form indicators of each level structure, the agglomeration characteristics of science and education cultural facilities in Guiyang were analyzed. The research showed that the resource distribution of science and education cultural facilities was imbalanced, the distribution of high-level clusters was significantly different, and it was difficult to realize the sharing of science and education cultural resources. In the research, DBSCAN algorithm was used to identify clusters of science and education cultural facilities, which provided a theoretical method for deeply mining the urban geography POI information, and a support for quantitative cognition of urban physical space planning, and optimization of urban resource allocation.
作者
刘甜甜
齐述华
Liu Tiantian;Qi Shuhua(Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi NormalUniversity, Nanchang Jiangxi 330022;School of Geography and Environment, Jiangxi Normal University, Nanchang Jiangxi 330022)
出处
《创新科技》
2019年第2期18-24,共7页
Innovation science and technology
关键词
DBSCAN
空间聚类
集群识别
空间分析
贵阳
DBSCAN spatial clustering
cluster identification
spatial analysis
Guiyang
