Nonstationary Spatial Interpolation Method for Urban Model Development
Bibliographic record
Abstract
In some situations in urban modeling practice, data cannot be preserved at the highest possible level of resolution. For example, when data from different sources are collated, the areal partitioning systems may not be compatible with each other. In other cases, related but separated models (e.g., urban transportation–land use and environmental models) may have been designed to operate at different spatial scales, posing a challenge to any efforts to link them. In these and similar situations, a method for interpolating data is required to produce compatible zoning systems or data at a desired level of resolution. A nonstationary (location-specific) spatial interpolation method, which has various potential applications in transportation as well as urban modeling, is proposed. The method combines the concept of location-specific parameters of a geographically weighted regression model and the concept of variogram function of kriging used to model spatial autocorrelation. Two case studies are presented to illustrate the application of the method in situations that are common in urban and transportation analysis. The results suggest that the method can be a useful alternative for spatial interpolation when nonstationarity and spatial autocorrelation appear co-incidentally in the analysis. The model is therefore expected to help to improve the performance of urban models by providing more accurate data at desired levels of resolution.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".