基于图结构的空间同位模式挖掘  

Spatial co-location pattern mining based on graph structure

作　　者：王靖涵 艾廷华[1,2] 吴昊 徐海江[1,2] 栗广岳 WANG Jinghan;AI Tinghua;WU Hao;XU Haijiang;LI Guangyue(School of Resource and Environmental Sciences,Wuhan University,Wuhan 430079,China;Key Laboratory of Geographic Information System,Ministry of Education,Wuhan 430079,China;State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan 430079,China)

机构地区：[1]武汉大学资源与环境科学学院,湖北武汉430079 [2]地理信息系统教育部重点实验室,湖北武汉430079 [3]测绘遥感信息工程国家重点实验室,湖北武汉430079

出　　处：《测绘学报》2024年第4期724-735,共12页Acta Geodaetica et Cartographica Sinica

摘　　要：空间同位模式反映了不同地理要素分布的依存关系,是地理学第一定律的体现,也符合空间大数据分析重在揭示事物关联特征的目标。空间同位模式挖掘需要顾及空间分布耦合机制,探测空间邻近关系及基于支持度等指标分析高频共生模式。现有方法多在判定邻近关系的同时搜索共生模式,导致在挖掘高阶共生模式时需要实时修正几何邻近关系,在复杂系统下丧失计算过程的灵活性。考虑到图数据蕴含的拓扑连接信息与空间同位模式相契合,本文提出一种基于图结构的空间同位模式挖掘方法。该方法一步完成几何上的邻近关系探测,然后在图数据库中通过子图搜索完成逻辑上的同位模式判别。首先,基于Delaunay三角网构建自适应邻接图,利用自适应邻接过滤器删除无效连接。然后,通过候选子图的不断连接、剪枝、生长,逐步从N元递推获取N+1元候选同位模式。最后,通过计算支持度指标并与预定义阈值比较以确定空间同位模式。本文基于不断生长迭代的图遍历思想提升了空间同位模式挖掘面向更复杂的空间场景的普适性。试验表明本文方法具备高效的挖掘能力,相较传统算法,在多元空间同位模式的挖掘任务中效果更优。Under the first law of geography,spatial co-location patterns reflect the dependency of different geographic elements’distribution,satisfying the association discovery of big spatial data analysis.Spatial co-location pattern mining needs to consider the spatial conjunction mechanisms,detect spatial neighborhood relationships and search high-frequency patterns with metrics such as support.The common co-location mining methods usually combine geometric computation and logical reasoning,which resulting in the need to correct geometric neighborhoods while mining higher-order co-location patterns.Considering that the topological information contained in graph data is suited to spatial co-location pattern,this study proposes a graph structure-based co-location pattern mining method that completes the geometric proximity detection in one step,and then completes the logical co-location pattern discrimination by subgraph search in the graph database.Firstly,we construct the adjacency graph based on the Delaunay triangle network and use an adaptive adjacency filter to eliminate invalid connections.Second,the N+1 elements of candidate co-location patterns are obtained recursively from the N elements through continuous joining,pruning,and growing of subgraphs.Finally,the spatial co-location patterns are determined by calculating the support metrics and compared with predefined thresholds.Based on the concept of continuous graph traversal,this study improves the generality of spatial co-location pattern mining in complex scenarios.Experiments show that this method is more efficient than traditional algorithms,with better results in multivariate spatial co-location pattern mining.

关 键 词：空间同位模式 自适应邻接图 图遍历 APRIORI算法 

分 类 号：P208[天文地球—地图制图学与地理信息工程]