Exploring urban accessibility scores using multi-criteria decision analysis techniques
Bibliographic record
Abstract
This paper explores alternative methods in which an urban walkability score may be determined. Walk Score is a popular urban accessibility index which determines the walkability of a neighbourhood or specific address by measuring the proximity of the location to nearby services and amenities. Traditional walkability scores, such as Walk Score, are limited because of their inability to vary the importance of being in proximity to certain services and amenities. Multi-criteria decision analysis (MCDA) techniques, specifically simple additive weighting (SAW) and ordered weighted averaging (OWA), provide a geographic approach to determining the walkability of an area and allow users to determine the weights of importance of all services and amenities. MCDA-based walkability scores were calculated and compared to one another and to Walk Score. Both SAW and OWA methods created similar walkability indexes for dissemination areas throughout Toronto. However, the MCDA results could not be directly compared to Walk Score, as there was a significant difference between the value ranges of the scores. Thus, the 140 Toronto neighbourhoods were ranked from most to least walkable for the MCDA-based methods and Walk Score, based upon each method’s respective scores. Upon comparison, it was evident that both Walk Score’s methodology and the MCDA-based methodologies resulted in similar outcomes of walkability rankings for Toronto neighbourhoods
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".