矢量居民地多边形多级图划分聚类方法

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...

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

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.