Development and application of knowledge graph-based spatiotemporal data model for urban physical examination
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".