An RP-MCE-SOP Framework for China’s County-Level “Three-Space” and “Three-Line” Planning—An Integration of Rational Planning, Multi-Criteria Evaluation, and Spatial Optimization
Bibliographic record
Abstract
“Three-space” (including agricultural space, urban and rural construction space, and ecological space) and “three-line” (including urban development boundary, prime farmland control line, basic ecological control line) planning has been regarded as an essential measure for China’s city and county level “multiple-plan integration”. It handles the multiple planning objectives of development management, agricultural land preservation, and ecological resource protection. This article proposes a rational planning with multi-criteria evaluation and spatial optimization (RP-MCE-SOP) framework for China’s county-level “three-space” and “three-line” planning by following the rational planning (RP) model and taking advantages of multi-criteria evaluation (MCE) and spatial optimization (SOP) techniques. The framework includes five steps of building the SOP model, land suitability evaluation with MCE, optimization problem solving, post-processing of land allocation solutions, and applying post-processed solutions to “three-space” and “three-line” planning. The framework was implemented in Dongxihu District of Wuhan City with the Boolean aggregation and analytical hierarchy analysis (AHP) MCE techniques and the patch-based Non-dominated Genetic Algorithm (NSGA-II) SOP algorithm. The case study shows: (1) The framework is feasible and useful for assisting decision making in “three-space” and “three-line” planning. (2) The planning solutions protect ecologically sensitive spaces and high-quality agricultural land and plan future construction in the urban peripheral area or transportation convenient areas. (3) The solutions are useful for planning the hard boundaries for ecological resource protection and prime farmland preservation and setting both hard and soft boundaries for urban growth.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".