A geographic knowledge integrated computation framework based on grid graph modelling

Abstract. Managing dynamic geographic knowledge effectively is hindered by fragmented tools lacking holistic integration, particularly when handling the heterogeneous and evolving nature of real world spatio-temporal data. Traditional knowledge graphs and databases struggle with efficient representation, storage, and reasoning over such complex information. This paper propose an integrated computation framework built upon Grid Graph Modelling to make geography computable. This framework provides an end-to-end solution encompassing knowledge representation, storage, querying, and spatio-temporal reasoning. It synergistically integrates three core components: the Grid Augmented Geographic Knowledge Graph (AugGKG) for unified grid based representation with computable spatial relations; the Grid Graph Database (GGD) for spatially aware storage and efficient grid algebra based computation; and the Grid Neighborhood-based Graph Convolutional Network (GN-GCN) for advanced reasoning by learning from semantic, spatial grid, and temporal dimensions. This cohesive architecture transforms diverse geographic data into actionable knowledge, enabling efficient querying and complex reasoning, paving the way for next generation intelligent geospatial systems, including empowering foundation models, enhancing smart cities, creating digital twins, and reasoning geographic event evolution.