A Declarative Geographic Knowledge Graph Framework for District-Scale Urban Infrastructure Analysis

Abstract

Geographic knowledge graphs (GeoKGs) have attracted growing attention for urban digital-twin systems, yet prior work has primarily targeted 3D-visualisation efficiency on small scenes rather than district-scale infrastructure analysis. This paper proposes a declarative GeoKG framework that redirects the focus toward analytical capabilities at district scale, with three contributions. First, a declarative rule engine of 7 matching strategies and 18 rules automatically generates 936,739 relationships from 118,231 nodes on Yuseong-gu (Daejeon) and 1,402,275 from 320,863 nodes on Sejong, with 88–100% independent cross-validation precision for attribute- and proximity-based rules. Second, a physical road-network topology built from TN_RODWAY_NODE/LINK and TL_SPRD_MANAGE delivers 100% topological coverage of the building–road linkage on both cities, with entrance-aware FRONTS_ROAD anchoring 98.4% of buildings and the underlying positional quality decomposed quantitatively in the precision-validation section. Third, 16 graph-based analysis functions—including safety assessment, dead-zone identification, and road-closure impact simulation—support evidence-based urban management. End-to-end Neo4j builds complete in ∼6 min on Yuseong and ∼22 min on Sejong, with sub-2% rule-engine run-to-run variance across ten independent builds per region; the Sejong/Yuseong engine ratio (3.11×) closely tracks the 2.92× ratio of underlying pair operations, confirming near-linear scaling. Because the rules are declarative JSON specifications independent of any region, the framework is portable to other cities providing equivalent open data. Case studies reveal infrastructure inequality across 45 stable legal-dong of Yuseong (safety-score range 18.5–79.8, 4.3× best-to-worst gap) and 131 stable legal-dong of Sejong (30.4–80.0, 2.6× gap), with the gap concentrated in suburban areas with sparse object-address shelter coverage.