Revealing association rules within intricate ecosystems: A spatial co-location mining method based on Geo-Eco knowledge graph

Abstract

The analysis of association rules within ecosystems is crucial for monitoring, managing, and conserving natural resources. As widely adopted approaches for this task, geospatial methods involving spatial co-location pattern mining can reveal distribution rules and inherent associations among diverse geographical elements. Rooted in Tobler’s first law of geography, these methods focus on the impact of spatial proximity. However, apart from proximity, heterogeneity of environmental attributes such as elevation, temperature and precipitation are also essential for the formation of associations. For environmental co-location (Eco-location) pattern detection, we propose a method based on the Geo-Eco Knowledge Graph (GEKG) to mine multi-impact association rules. Firstly, we introduce the Adaptive Threshold (AT) to constrain the Delaunay triangular network, dynamically regulating adjacency relationships to generate geo-eco knowledge graph’s skeleton. For comprehensive ecosystem representation, various environmental attributes are integrated as semantic information into GEKG. In the reasoning of Eco-location patterns, we innovate beyond the traditional co-location paradigm by considering both spatial proximity and semantic similarity. Under the impact of various environmental information, sub-sets of geographically proximate entities are extracted to detect Eco-location patterns. For effective management and efficient computation, we utilize the Neo4j graph database to manage large-scale GEKG and mine Eco-location patterns with its graph search function. Experiments conducted on simulated and real-world ecological datasets show that, compared to existing techniques, our GEKG-based method can detect Eco-location patterns with greater accuracy and efficiency.

Keywords

• Geo-eco knowledge graph
• Spatial co-location pattern
• Environmental similarity
• Neo4j graph database