Towards a <scp>spatio‐temporal</scp> multicriteria evaluation method: A suitability analysis of residential units in a <scp>3D</scp> urban environment
Bibliographic record
Abstract
Abstract Spatial multi‐criteria evaluation (MCE) techniques aid urban planning management by analyzing decision problem alternatives for solutions to help inform decision‐making. However, there is a lack of such methods that incorporate the temporal dimension, an important factor when analyzing the dynamic urban landscape and decisions surrounding its changes. A novel spatio‐temporal MCE approach is proposed that operates in three‐dimensional (3D) space and time to identify changing suitability values of decision alternatives. This space–time method is implemented to evaluate the suitability of residential units over a 15‐year period in part of downtown City of Vancouver, Canada. The results indicate that the majority of units exhibit a decrease in suitability with time due to depreciation and reduction of assets like view and privacy from the construction of new buildings. The proposed method can be used by urban planners and developers to assist in long‐term assessments of proposed development scenarios and their impact on existing urban infrastructure.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".