Spatial co-location pattern mining based on graph structure
Under the first law of geography,spatial co-location patterns reflect the dependency of different geographic ele-ments'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 reason-ing,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 com-pletes the logical co-location pattern discrimination by subgraph search in the graph database.Firstly,we construct the adja-cency graph based on the Delaunay triangle network and use an adaptive adjacency filter to eliminate invalid connections.Sec-ond,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.
万方数据
维普
