Development and application of knowledge graph-based spatiotemporal data model for urban physical examination

The urban physical examination is pivotal in diagnosing and resolving ‘urban diseases’. However, it encounters challenges including the intricate interconnections among heterogeneous data, the spatiotemporal differences in urban pathologies, and the multi-dimensional scenario-oriented representation. A knowledge graph is a potent technical instrument capable of depicting data replete with relational details and capturing the dependency ties among entities. In view of the above, this paper proposes a knowledge graph-based spatiotemporal data model for the urban physical examination. The model focuses on application scenarios, and builds a conceptual model with a three-layer architecture of ‘semantics-data-scenario’ and an ontology logic model structured as a hypergraph. This is intended to facilitate the management of spatiotemporal data throughout the process, while also enabling scenario-based spatiotemporal expression. Furthermore, this paper presents an in-depth analysis of the urban greenway construction case. The results show that the model organizes and expresses urban data and knowledge, covering multiple levels (such as themes, connotations, knowledge points, and indicators) and multiple granularities (macro-city, meso-region, micro-street), helping to understand urban physical examination from multiple dimensions. This not only provides a scientific basis for urban planners to make decisions but also sets an example for the practice of urban physical examination knowledge service.