基于Voronoi图的空间点事件统计聚类方法  

Statistical clustering method of spatial point events based on Voronoi diagram

作　　者：刘敬一 唐建波[2,3] 郭琦 姚晨 陈金勇 梅小明[2] LIU Jingyi;TANG Jianbo;GUO Qi;YAO Chen;CHEN Jinyong;MEI Xiaoming(China Electronics Technology Group Corporation 54th Research Institute(CETC 54),Shijiazhuang 050081,China;Department of Geo-informatics,Central South University,Changsha 410083,China;Hunan Geospatial Information Engineering and Technology Research Center,Changsha 410083,China)

机构地区：[1]中国电子科技集团公司第五十四研究所,石家庄050081 [2]中南大学地理信息系,长沙410083 [3]湖南省地理空间信息工程技术研究中心,长沙410083

出　　处：《时空信息学报》2024年第2期205-215,共11页JOURNAL OF SPATIO-TEMPORAL INFORMATION

基　　金：中国博士后科学基金(2021M703021);河北省人才择优自主基金(B2021003031);湖南省自然科学基金项目(2021JJ40727,2022JJ30703)。

摘　　要：挖掘地理空间数据中点事件聚集模式对于揭示流行疾病、犯罪分布热点区域及城市基础设施空间分布格局等具有重要意义。针对不同形状、密度和大小的显著空间点聚集模式的识别,目前以空间扫描统计为代表的方法虽然可以对空间点聚类的显著性进行统计推断,减少虚假聚类结果,但其主要用于识别球形或椭圆形状的聚簇,对于沿着街道或河道分布的任意形状、不同密度的显著空间点聚簇识别还存在局限。因此,本研究提出一种基于Voronoi图的空间点聚集模式统计挖掘方法。首先,采用Voronoi图来度量空间点分布的聚集性,将空间点聚类问题转化为热点区域探测问题;其次,结合局部Gi*统计量探测统计上显著的空间点聚簇;最后,通过模拟数据和真实犯罪事件数据进行实验与对比分析。结果表明:本方法能够有效探测任意形状的空间点聚类,并对空间点簇的显著性进行统计判别,识别显著的空间点簇,减少随机噪声点的干扰;聚类识别结果优于现有代表性方法,如DBSCAN算法、空间扫描统计方法等。Clustering spatial point events aims to identify hotspots where these events occur frequently in specific spatial regions.Exploring clustering patterns of spatial point events in geospatial data is crucial for disease outbreak warning,crime hotspot analysis,urban facilities planning,and many other fields.Classical methods exist for recognizing spatial point aggregation patterns of different shapes,different densities,and sizes.However,most of these methods lack statistical discrimination of spatial point aggregation patterns,potentially leading to unreliable clustering results.For example,existing clustering algorithms like k-means,DBSCAN,AUTOCLUST may group randomly distributed spatial points into clusters.While methods like spatial scan statistics can statistically infer the significance of spatial point clustering patterns,they are mainly designed for identifying clusters with spherical or elliptical shapes.Limitations arise in recognizing significant spatial point clusters with arbitrary shapes and different densities,especially along streets or rivers.Recently,many attentions have been paid to clustering of spatial point events,discovery of statistically significant point clusters with varied densities and irregularly shape is still a challenge work.In order to identify statistically significant spatial point clusters with different shapes,densities,and sizes,a statistical spatial point clustering method based on the Voronoi diagram is proposed in this paper.The Voronoi diagram is initially constructed based on the original spatial points to measure the aggregation of their distribution.A smaller Voronoi cell area indicates a more clustered distribution of points around a spatial point.Using the Voronoi diagram,this paper transforms the spatial point clustering problem into a task of detecting spatial hotspots the commonly used local Gi*statistic is used to detect statistically significant hotspots(high-density points or seed points)in local areas.Further,these high-density points or seed points are used to

关 键 词：空间点聚类 显著模式 空间数据挖掘 统计检验 犯罪热点分析 VORONOI图 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]