Urbanization-driven risk assessment of song-dynasty cultural heritage under land-use transition: A landscape-based spatial model for historic Kaifeng, China
Bibliographic record
Abstract
Urbanization and land-use transition are major drivers of cultural heritage risk in historic cities, yet the spatiotemporal dynamics and underlying mechanisms remain underexplored. This study presents an integrated risk assessment framework for Song-Dynasty heritage in Kaifeng, Central China, systematically coupling multi-temporal land-use data, urban expansion intensity (UEI), landscape pattern metrics, and a Natural–Social–Landscape (NSL) risk model based on analytic hierarchy process (AHP). Using remote sensing and Geographic Information System (GIS) data from 1985 to 2022, we reveal that Kaifeng has undergone significant conversion from Cropland to Impervious, with urban expansion intensity and landscape fragmentation accelerating particularly around heritage clusters. Results indicate that risk hotspots are dynamic, shifting from peri-urban zones in early stages to the core heritage buffer in recent years, driven by the interplay of urban expansion and landscape fragmentation. The NSL model quantitatively captures the combined influence of natural, social, and landscape factors, enabling dynamic mapping of risk zones. Comparative analysis with recent high-impact literature demonstrates both consistency with broader urban–heritage risk patterns and novel insights into the spatial heterogeneity and temporal evolution of risk. These findings highlight the necessity of adaptive, spatially-targeted conservation policies and provide a replicable methodological reference for risk-informed urban planning in heritage-rich regions. • A spatial risk assessment model is developed for Song-dynasty heritage in Kaifeng. • Urban expansion intensity and landscape metrics are integrated via GIS-based analysis. • AHP-weighted NSL model identifies dynamic high-risk zones from 1985 to 2022. • Fragstats-derived LPI, SHDI, and PAFRAC reveal heritage landscape sensitivity. • The study proposes spatial regulation strategies for heritage-informed urban planning.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".