矢量居民地多边形多级图划分聚类方法  

A Multi-level Graph Partition Clustering Method of Vector Residential Area Polygon

作　　者：金澄[1,2] 安晓亚 陈占龙[3] 马啸川 JIN Cheng;AN Xiaoya;CHEN Zhanlong;MA Xiaochuan(Xi'an Research Institute of Surveying and Mapping,Xi'an 710054,China;State key Laboratory of Geo-Information Engineering,Xi'an 710054,China;School of Geography and Information Engineering,China University of Geosciences(Wuhan),Wuhan 430074,China)

机构地区：[1]西安测绘研究所,陕西西安710054 [2]地理信息工程国家重点实验室,陕西西安710054 [3]中国地质大学(武汉)地理与信息工程学院,湖北武汉430074

出　　处：《武汉大学学报（信息科学版）》2021年第1期19-29,共11页Geomatics and Information Science of Wuhan University

基　　金：国家自然科学基金(41871305);国家重点研发计划(2017YFC0602204);中央高校基本科研业务费(CUGQY1945);中央高校基本科研业务费(GLAB2019ZR02);地质探测与评估教育部重点实验室主任基金。

摘　　要：针对复杂居民地多边形的信息挖掘问题,提出了一种多级图划分聚类分析方法,构造居民地多边形的图模型,并通过对图模型进行粗化匹配与重构、初始化分和细化得到聚类结果。首先构建研究区域内居民地建筑物的Delaunay三角网,生成包含研究对象之间的邻接信息图;然后结合空间认知准则和人类认知的特点,采用形状狭长度、面积比、凹凸性、距离和连通性5个指标度量邻接图的相似性;最后应用多级图划分方法,得到聚类结果。采用中国上海地区的居民地建筑物矢量数据进行聚类分析实验,并对比了改进的k均值算法(k-Means++)、具有噪声鲁棒性的基于密度的空间聚类算法(density-based spatial clustering of applications with noise,DBSCAN)和最小生成树(minimum spanning tree, MST)聚类算法得到的轮廓系数以及视觉效果。实验结果表明,基于多级图划分的居民地多边形聚类分析的结果更加符合人类认知。In order to solve the problems of information mining of complex residential polygons,a multilevel graph partition clustering method is proposed to construct the graph model of residential polygons,and the clustering results are obtained by coarsen,matching and reconstruction,initialization and refinement of the graph model.Firstly,the Delaunay triangular network of residential buildings in the study area is constructed to generate the adjacent information graph including the research objects.Then,the similarity of the neighborhood graph is measured by five indexes of shape narrow length,size,convexity,distance and connectivity combined with the characteristics of spatial cognition and human cognition in this paper.Finally,the clustering results are obtained by using the multi-level graph partition method.In the experiment,the vector data of residential buildings in Shanghai are used for clustering analysis,and the silhouette coefficients and visual effects of improved k-Means algorithm(k-Means + +),density-based spatial clustering of applications with noise(DBSCAN) and minimum spanning tree(MST) clustering algorithms with noise robustness are compared.The experimental results show that the results of polygonal clustering analysis based on multi-level graph partition are more consistent with human cognition.

关 键 词：居民地多边形 相似性度量 多边形聚类分析 多级图划分方法 

分 类 号：P208[天文地球—地图制图学与地理信息工程]