| 顾及属性空间分布不均的空间聚类方法——以城市商业中心的提取为例 |
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| 引用本文： | 朱杰,孙毅中,陈律余,周卫,孟耀伟.顾及属性空间分布不均的空间聚类方法——以城市商业中心的提取为例[J].武汉大学学报(信息科学版),2017,42(12):1696-1702. |
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| 作者姓名： | 朱杰 孙毅中 陈律余 周卫 孟耀伟 |
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| 作者单位： | 1.南京师范大学虚拟地理环境教育部重点实验室, 江苏, 南京 210023 |
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| 基金项目： | 国家自然科学基金41671392国家自然科学基金41371374国家自然科学基金41301405公安部科技强警基础工作专项项目2015GABJC39 |
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| 摘 要： | 针对Delaunay三角网空间聚类存在的不足，提出一种顾及属性空间分布不均的空间聚类方法。首先将Delaunay三角网空间位置聚类作为约束条件，采用广度优先搜索方法，以局部参数"属性变化率"作为阈值识别非空间属性相似簇的聚类过程。以城市商业中心为例，验证了该方法能够更客观地识别非空间属性相似的簇，且自适应属性阈值可以满足不同聚类需求，为城市商业中心等空间实体的提取提供了一种有效方法。
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| 关 键 词： | 空间聚类 Delaunay三角网 属性空间分布不均 属性阈值 城市商业中心 |
| 收稿时间： | 2016-09-14 |
A Spatial Clustering Method Based on Uneven Distribution of Non-spatial Attributes——Identifying City Commercial Center |
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| Institution: | 1.Key Laboratory of Virtual Geographic Environment of Ministry of Education, Nanjing Normal University, Nanjing 210023, China2.Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China |
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| Abstract: | Spatial clustering is an important tool for spatial data mining and spatial analysis. It is important for the clustering results in many applications to meet the requirement that spatial objects in the same cluster are similar in both the spatial and the attribute domains. To solve the problems of existing methods based on Delaunay triangulation, this paper proposes a spatial clustering method considering uneven distribution of non-spatial attributes. The proposed algorithm involves two main steps:the first is to construct spatial proximity relationships, and the second is to cluster spatial objects with similar attributes. Delaunay triangulation with edge length constraints is first employed to construct spatial proximity relationships among objects. To obtain satisfied results in spatial clustering with attribute similarity, the breadth first traverse (BFT) clustering algorithm is used in accordance with variation rate of attribute to adapt to the local change information of attribute distribution. The performance of the proposed algorithm was evaluated experimentally through comparison with one of the leading state-of-the-art alternatives:multi-constraints algorithm. The results show that our method outperforms the comparative algorithm as attributes are unevenly distributed in space, and provides a quantitative research method in city commercial center extraction. The effectiveness and practicability of the proposed algorithm illustrates three advantages of our algorithm:i) it can reflect the tendency of the entity attribute in the spatial distribution; ii) it can meet the requirement that attributes are unevenly distributed in space; iii) it can discovery clusters with arbitrary shape and is robust to outliers. |
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