A method for estimating localised space-use pattern and its applications in integrated land-use transport modelling
Bibliographic record
Abstract
Contemporary integrated land-use transport models (ILUTMs) explicitly consider interactions between floorspace demand/supply and rent at fine spatial scales, which requires a good understanding between floorspace use pattern and competition of locations among socioeconomic activities. Floorspace use patterns are usually represented by space use coefficients (SUCs) by activity type by zone, which are then used to develop theoretical space-use-rent curves (SURCs), in order to reflect the elasticity between rent and floorspace consumption rates. Literature review indicates that existing studies mostly use borrowed SUCs or subjective judgement methods for synthesising base-year floorspace and developing SURCs. In general, their accuracy is largely unknown and synthesised floorspace could be highly inaccurate. In this study, a linear programming method is proposed to estimate localised SUCs by assuming that zonal population, employment and floorspace total data are available. Study results show that the method can provide localised SUCs and better SURCs than traditional methods. It is found that, as the size of the homogeneous optimisation areas (HOAs) decreases, the accuracy of zonal space totals estimated increases considerably. For example, the estimation error between the observed and estimated zonal space totals reduces from 76.2% under the most aggregate case to 24.7% under the most disaggregate case. The sum of square errors (SSEs) between the optimised SUCs and the SURCs also reduces to about one-quarter of their original values. The method proposed contributes to a procedural process to estimate localised SUCs with known accuracy, which is proved to be a better alternative to traditional synthesis methods.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".