An integrated graph-spatial method for high-performance geospatial-temporal semantic query

• Bridging property graphs and spatial engines for efficient geospatial-temporal semantic queries. • Cross-database spatiotemporal indexes accelerate graph traversal and pattern matching. • Supporting customizable geospatial-temporal analysis to enhance semantic representation capabilities. • Lightweight and low-code architecture facilitates integration with existing GIS platforms. Knowledge graphs (KGs) have gained significant attention in the GIS community as a cutting-edge technology for linking heterogeneous and multimodal data sources. However, the efficiency of semantic querying of geospatial-temporal data in KGs remains a challenge. Graph databases excel at handling complex semantic associations but exhibit low efficiency in geospatial analysis tasks, such as topological analysis and geographic calculations, while relational databases excel at geospatial data storage and computation but struggle to efficiently process association analysis. To address this issue, we propose GraST, a geospatial-temporal semantic query optimization method that integrates property graphs and relational databases. GraST stores complete geospatial-temporal objects in a relational database (using built-in or extended spatial data engines), and employs spatiotemporal partitioning and indexing to enhance query efficiency. Simultaneously, GraST stores lightweight geospatial-temporal nodes in the graph database and links them to multi-granularity time tree and Geohash encoding nodes to enhance spatiotemporal aggregation capabilities. During query processing, user queries are broken down into graph semantic searches and geospatial calculations, pushed down to the graph and relational database for execution. Additionally, GraST adopts the two-phase commit protocol for cross-database data synchronization. We implemented a GraST prototype system by integrating PostGIS and Neo4j, and conducted performance evaluations and case studies on large-scale real-world datasets. Experimental results demonstrate that GraST shortens query response times by 1–2 orders of magnitude and offers flexible support for diverse geospatial-temporal semantic queries.