KDSR: knowledge-guided dynamic-static integrating scenario representation for urban physical examination

Dynamic scenario representation for urban physical examination plays a vital role in facilitating urban governance. However, prevailing urban scenario visualization suffers from two critical limitations: overemphasis on data presentation while neglecting knowledge association, and disjunction between static and dynamic components without inherent spatiotemporal logic. Therefore, this paper proposes KDSR—a knowledge-guided dynamic-static integrating scenario representation for urban physical examination—comprising three core steps: first, it conducts an in-depth analysis of business requirements and scenario characteristics to construct a scenario knowledge graph for urban physical examination; second, guided by the knowledge graph, it models and reorganizes urban physical examination scenarios, and then uses spatiotemporal narrative logic to achieve scenario generation and dynamic update; finally, it establishes a dynamic-static collaborative visual variable enhancement for semantic information and a data optimization scheduling presentation. An empirical study was conducted based on Guangzhou’s urban physical examination, selecting three typical scenarios for dynamic representation and analysis. Results show that KDSR significantly improves the practicality, comprehensibility, and visualization efficacy of scenario information, reducing decision-making time by 55.3% and increasing accuracy by 18.7%, thus effectively meeting business needs. This research holds substantial application value and practical significance for advancing knowledge services in urban governance.