KDSR: knowledge-guided dynamic-static integrating scenario representation for urban physical examination
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".