一种面向精细化地理分区的空间约束聚类方法  

A spatially constrained clustering method for fine-scale geographical partitioning

作　　者：丘铂钧 贾嘉楠 徐柱[1] QIU Bojun;JIA Jianan;XU Zhu(Faculty of Geosciences and Engineering,Southwest Jiaotong University,Chengdu 611756,China)

机构地区：[1]西南交通大学地球科学与工程学院,成都611756

出　　处：《时空信息学报》2024年第3期359-369,共11页JOURNAL OF SPATIO-TEMPORAL INFORMATION

基　　金：国家重点研发计划项目(2022YFB3904202);国家自然科学基金重大项目(42394063)。

摘　　要：在空间分区的相关研究中,虽然已有经典聚类算法k均值聚类(k-means)结合空间约束的成果,但其对于连续平铺面状地理要素的空间聚类适用性不高。因此,本文开展对k-means算法进行空间约束的探讨。通过改进SKATER算法的空间约束方式,构建一种包含自然扩张与次优扩张过程的空间约束的k-means算法;并在两个公共数据集上与已有研究方法进行比较评价。结果表明:本文方法尤其适用于处理连续平铺面状地理要素的分区;通过轮廓系数、DB指数及总残差平方和三个评价指标知,本文方法优于已有的SKATER、AZP及SC k-means方法。研究成果不仅能够为地理信息系统中的空间数据处理提供新的工具,也为聚类算法的研究提供了新的视角。As a classic clustering algorithm,the k-means algorithm is widely popular due to its simplicity and efficiency in the iterative classification process.However,when applied to specific spatial partitioning tasks,the traditional algorithm shows certain limitations because it either fails to consider spatial constraints or imposes them excessively.This research aims to address these limitations of traditional k-means clustering algorithm and the SKATER algorithm in spatial partitioning tasks.The study introduces an innovative approach by enhancing the k-means algorithm with spatial constraints,refining the methodology used in the SKATER algorithm to better accommodate the specific needs of spatial data analysis.This new method seeks to provide a more robust framework for clustering that respects both the attribute similarity and spatial contiguity of tessellated planar geographic features.The enhanced clustering algorithm,termed spatially constrained k-means,integrates spatial constraints directly into the clustering process to ensure that members of the same cluster are contiguous in space.This integration is achieved by modifying the clustering operation to prioritize spatial connections during each iteration.The method does not rely on traditional objective functions or heuristic approaches;instead,it expands clusters based on direct spatial adjacency,ensuring that the clustering process naturally adheres to the geographic continuity of the data.The effectiveness of this approach was tested using two distinct datasets:the 2020 urban population data from China and historical socio-economic data from 1930s France.These datasets were chosen to illustrate the algorithm’s capability across different types of spatial data and scales.The performance of the proposed method was benchmarked against traditional spatially constrained k-means,the SKATER algorithm,and other spatial methods using criteria such as visual coherence and numerical indices like the Davies-Bouldin index and silhouette coefficient.The spatially cons

关 键 词：聚类分析 空间数据处理 K-MEANS算法 地理信息系统 空间约束 空间分区 聚类质量改进 数据科学 

分 类 号：P208[天文地球—地图制图学与地理信息工程]