GeoGraphJSON: A lightweight semantic data model integrating spatial geometry and graph connectivity for AI-Driven spatial reasoning

Abstract. This paper presents GeoGraphJSON, a lightweight semantic data model that integrates spatial geometry with graph-based connectivity to support advanced spatial analysis and GeoAI applications. Conventional geospatial formats primarily focus on geometry and attributes, requiring relationships such as adjacency, containment, and connectivity to be derived dynamically through computationally intensive spatial operations. In contrast, GeoGraphJSON encodes these relationships explicitly as typed edges within a unified, interoperable structure. The proposed model introduces a hierarchical Unique Identifier (UID) system to ensure consistent lineage and cross-layer linkage, along with a structured edge framework to represent containment, adjacency, functional, and administrative relationships. A validation-driven methodology is developed to transform multi-layer geospatial datasets into graph-enhanced representations through data standardization, UID assignment, and rule-based edge generation.

The framework is implemented using a large-scale urban dataset from Riyadh comprising approximately 10,859 nodes and 13,733 edges across administrative, transportation, and urban asset layers. Graph-based evaluation demonstrates realistic spatial patterns, including right-skewed degree distribution, strong network connectivity, and identifiable community structures. Comparative analysis shows that GeoGraphJSON effectively bridges the gap between geometry-centric GIS formats and graph-based models, enabling efficient querying and direct integration with graph analytics workflows. The results highlight the model’s scalability and potential for supporting urban analytics, digital twins, and AI-driven geospatial systems.