基于Max-min distance聚类算法的园地空间聚类--以永泰县嵩口镇为例  

Garden Spatial Clustering Based on Max-min DistanceClustering Algorithm:Taking Songkou Town,Yongtai County as an Example

作　　者：冯宇琳 FENG Yulin(The Academy of Digital China,Fuzhou University,Fuzhou 350002,China)

机构地区：[1]福州大学数字中国研究院(福建),福建福州350002

出　　处：《测绘与空间地理信息》2024年第7期146-149,共4页Geomatics & Spatial Information Technology

摘　　要：空间聚类是空间数据挖掘的重要手段之一。本文研究了一种基于质心点距离的Max-min distance空间聚类算法:通过加载园地图斑数据,计算其园地图斑质心,判断聚类中心之间的距离,并将符合条件的园地图斑进行聚类,最终将聚类结果可视化表达。本文的算法是利用Visual Studio 2017实验平台和ArcGIS Engine组件式开发环境,采用C#语言进行编写。实验结果表明:1)Max-mindistance聚类通过启发式的选择簇中心,克服了K-means选择簇中心过于邻近的缺点,能够适应嵩口镇等山区丘陵地区空间分布呈破碎的园地数据集分布,有效地实现园地的合理聚类;2)根据连片面积将园地空间聚类结果分为大中小三类,未来嵩口镇可以重点发展园地连片规模较大的村庄,形成规模化的青梅种植园。Spatial clustering is one of the important means of spatial data mining.In this paper,a Max-min distance spatial clustering algorithm based on centroid point distance is studied:by loading the garden spot data,calculating the centroid of the garden spot,jud-ging the distance between the cluster centers,and classifying the eligible garden spots,it performs clustering and finally visualizes the clustering results.The algorithm in this paper is programmed in C#language using Visual Studio 2017 as the experimental platform and ArcGIS Engine component development environment.The experimental results show that:1)Max-min distance clustering im-proves the shortcomings of K-means selection of cluster centers that are too close by heuristically selecting cluster centers,and can a-dapt to the broken spatial distribution in mountainous and hilly areas such as Songkou town.The distribution of the garden data set can effectively realize the reasonable clustering of the garden;(2)According to the contiguous area,the spatial clustering results of the garden are divided into three categories:large,medium and small.The villages of large scale contiguous area in Songkou town can be developed to form a large-scale green plum plantation.

关 键 词：Max-mindistance聚类算法 园地 GIS 嵩口镇 

分 类 号：P208[天文地球—地图制图学与地理信息工程]