摘要： 城市功能分区是指运用有关模型和方法，使城市空间形成明确的功能单元和有序的空间结构，空间聚类是城市功能分区的一种常用方法。基于自组织映射神经网络，该文提出了一种组合式的城市功能区聚类方法，根据位置-属性一体化思想，综合考虑了影响城市功能分区的位置数据和属性信息，对城市功能区进行空间聚类计算。该方法挖掘了空间位置数据和属性信息中隐含的空间聚集信息，保证了城市功能分区结果的可靠性。实例分析表明，该方法的聚类结果可以为城市功能分区提供准确、可靠的依据。

关键词: 空间数据挖掘, 聚类, 自组织神经网络, 城市功能分区

Abstract: Urban function partitioning is one of the important tasks of urban land utilization and management. The goal of urban function partitioning is to make the urban land form a set of specific function units and regular spatial structures. Spatial clustering is the key process of urban function partitioning. Based on the principle of self-organizing neural network, this paper presents a combined-type of spatial clustering. That is to say, the attribute features and their spatial positions are processed by a unitive spatial clustering model. This method fully mines the connotative spatial clustering information in spatial attribute data and spatial positions. The experiment shows that the unitive spatial clustering method can provide a sufficient and reliable basis for urban function partitioning.

Key words: Spatial data mining, Clustering, Self-organizing neural network, Urban function partition

中图分类号: 

• TP311