Modelling spatial distribution of fine-scale populations based on residential properties
Bibliographic record
Abstract
Fine-scale population gridded datasets are of great significance in emergency response, resource allocation, and traffic planning. Many studies have developed fine-scale population spatialization models based on building patch area (BPA) and building floor (BF). However, little attention has been given to house occupancy rate (HOR). Based on BPA, BF and HOR, this study proposed a novel fine-scale population spatialization method, taking the six districts of Beijing as the study area. The results showed that the HOR in central Beijing was higher than that of the surrounding area. The model with consideration of HOR was more accurate than that without it. In addition, the fine-scale population gridded map generated by this novel method was more accurate (mean prediction error = 8.47%). For all the testing samples, the relative errors of the population gridded data were between −13.54% and 16.04%. Thus, this study suggested that HOR could be a key indicator for fine-scale population modelling. Furthermore, the proposed method could be employed in generating fine-scale population gridded maps and could provide credible and fundamental data for rapid response and decision-making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".