Big data fusion-driven geospatial knowledge graph construction method for sustainable smart cities

Urban planning faces increasing challenges in integrating and analyzing multi-source geospatial data due to inconsistencies in spatial resolution, data latency, and processing inefficiency. Traditional geographic information systems (GIS) and remote sensing models typically rely on a single data source, limiting their ability to deliver accurate and comprehensive insights for smart city development. This paper proposes a Big Data Fusion-Driven Geospatial Knowledge Graph framework (BDF-GeoKG) to address these limitations by integrating vector, raster, text, and image data. The proposed framework follows a structured process of entity extraction, relationship construction, attribute extraction, and entity alignment to establish a unified geospatial knowledge graph. Entity extraction identifies geographic objects and attributes from multi-source data. Relationship construction defines spatial and semantic connections between entities. Attribute extraction assigns detailed properties to entities, including spatial, environmental, and textual attributes. Entity alignment is achieved using road-based alignment strategies to ensure consistency across different data sources. The graph-based data model was implemented using Neo4j to support efficient storage, querying, and analysis of multi-modal data. Experimental validation using real-world data from Wuhan demonstrated the framework's effectiveness in urban heat island analysis, traffic flow monitoring, travel recommendations, and land use change detection. The results highlight the framework's potential to enhance data integration, support dynamic urban analysis, and provide intelligent decision-making support for sustainable smart city planning. Compared to traditional GIS workflows, BDF-GeoKG achieves comparable analytical accuracy while reducing query response time by over 40% and lowering the technical barrier for non-specialist users.