A Clustering Method of Rural Settlement Considering Direction Relation
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摘要: 农村居民地空间分布具有独特的规律性和复杂性，Voronoi图在表达居民地分布特征方面有显著优势。针对当前空间聚类较少考虑实体方向关系的问题，基于Voronoi图提出一种顾及方向关系的农村居民地聚类方法。首先，构建距离约束的Voronoi图，并构建居民地实体间的Voronoi邻近图；然后，利用无向特征与有向特征来综合评价居民地实体间的聚集强度；最后，消除聚集强度小于阈值的实体对的邻近关系，得到聚类结果。采用浙江省宁波地区部分农村居民地数据进行实验，结果表明，所提方法能够有效聚类不同分布模式的居民地，聚类结果符合人的认知习惯。
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关键词:
- 空间聚类 /
- 方向关系 /
- 农村居民地 /
- 距离约束的Voronoi图
Abstract:Objectives The spatial distribution of rural settlements has unique regularity and complexity. In order to reduce the difficulty of cartographic generalization, we can cluster rural settlements first. Voronoi diagram has significant advantages in expressing the distribution characteristics of settlements, but now spatial clustering seldom considers direction relation between entities. Direction relation is an important part of spatial relation. In theory, the introduction of direction relation in spatial clustering can help to improve the clustering effect. Therefore, based on Voronoi diagram, this paper proposes a clustering method of rural settlement considering direction relation.Methods First, Voronoi diagrams with distance constraint(DC-Voronoi) are constructed, and Voronoi proximity relations among settlement entities are determined. Second, undirected features are calculated based on the area of entities and the area of DC-Voronoi polygons. Directed features are calculated based on offset direction and offset distance of entities in DC-Voronoi polygons. Aggregation strength values of all entity pairs are calculated by combining undirected features and directed features. Finally, clustering result is obtained by eliminating the proximity of entity pairs whose clustering strength value is less than the threshold. Taken the data of rural settlements in Ningbo as an example, this paper sets silhouette coefficient as result evaluation index.Results and Conclusions Compared with the clustering results of density-based spatial clustering of applications with noise method and clustering by fast search and find of density peaks method, the results show that the proposed method can effectively cluster rural settlements with different distribution patterns, and can accurately identify the boundary of rural settlements. The clustering granularity is moderate, and the clustering results accord with people's cognitive habits. -
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图 7 实验区域局部聚类结果对比
Figure 7. Comparison of Local Clustering Results in Experimental Area Using Different Clustering Methods
表 1 居民地实体间的聚集强度值
Table 1 Aggregation Intensity Values Between Entities
居民地ID 面积比 偏离方向/（$ ° $） 偏离度 方位角/（$ ° $） 聚集
强度2052 0.897 209.75 0.018 240.50/60.50 35.883 2054 0.898 212.29 0.014 1484 0.003 91.45 0.732 119.69/299.69 5.946 1678 0.159 220.75 0.133 673 0.001 261.56 0.023 261.82/81.82 0.014 1347 0.003 76.33 0.019 3168 0.019 317.99 0.809 116.03/296.03 -0.535 3187 0.032 102.39 0.887 
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