标題: Graph-based spatial co-location pattern mining: integrate geospatial analysis and logical reasoning

作者: Wang, JH (Wang, Jinghan); Ai, TH (Ai, Tinghua); Wu, H (Wu, Hao); Xu, HJ (Xu, Haijiang); Xiao, TY (Xiao, Tianyuan); Li, GY (Li, Guangyue)

來源出版物: INTERNATIONAL JOURNAL OF DIGITAL EARTH  卷: 17  期: 1  文獻号: 2390434  DOI: 10.1080/17538947.2024.2390434  Published Date: 2024 DEC 31  

摘要: Spatial co-location patterns reflect the inherent correlations among geographical elements. Mining co-location patterns of POIs can provide valuable insights for urban planning and resource management. Generally, co-location mining comprises two steps: proximity relationship determination (geospatial analysis) and frequent pattern recursion (logical reasoning). Previous methods often separate these two steps: serializing proximity relationships to enumerate frequent sequences. However, this approach suffers from limited flexibility and intuitiveness: as continuous spatial contexts are segmented into numerous small parts, it fails to adequately represent geographic correlations and hinders the effective visualization of logical reasoning. Facing these challenges, this study proposes a novel graph-based spatial co-location mining method (GSCM), which leverages graphs to integrate geospatial analysis and logical reasoning. Initially, to establish adjacency relationships, GSCM constructs the adaptive neighborhood graph, which dynamically adjusts proximity thresholds to accommodate geographic heterogeneity. Subsequently, the Apriori logical recursive process is realized on the graph structure. By leveraging graph searching, pruning, and growing, the potential growth directions of co-location patterns are identified, enhancing both the efficiency and intuition of frequent pattern recursion. Through experiments conducted on large-scale POI datasets from Wuhan, GSCM is compared with existing baseline methods, verifying its potential to uncover co-location patterns in complex spatial contexts.

作者關鍵詞: Spatial co-location pattern mining; adaptive neighborhood graph; graph growth; POI; Neo4j database

地址: [Wang, Jinghan; Ai, Tinghua; Wu, Hao; Xu, Haijiang; Xiao, Tianyuan] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430072, Hubei, Peoples R China.

[Li, Guangyue] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & Re, Wuhan, Peoples R China.

通訊作者地址: Ai, TH (通訊作者)，Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430072, Hubei, Peoples R China.

電子郵件地址: tinghuaai@whu.edu.cn

影響因子：3.7