In Search of 20-Minute Neighborhoods: Toronto’s Pedestrian Network using Walkability to Nearest Amenities
Bibliographic record
Abstract
Abstract : This study aimed to construct and assess a pedestrian walkable network that would be used further to generate an analytical lens for the assessment of community walkability across the City of Toronto. In this study, we dealt with proximity analysis of pedestrian network in Toronto city by visualizing how “walkable” a destination is, in terms of access to different amenities like schools, libraries, hospitals, supermarkets, TTC stops and convenience stores. The basic approach of this study is first to create pedestrian networks as graphs of links and nodes, then calculate walking distance and minutes based on the accessibility of various amenities via pedestrian sidewalks networks and analyse pedestrian walkability. 20-minute neighbourhoods walkability has been calculated to determine how-walkable a particular area is in relation to pedestrian access to various amenities and socio-demographic characteristics. The overall result gives an overall walkability score or allows users to know if a given neighbourhoods are more walkable within 20 minutes. These analytical results provide policy opportunities for city planners to pursue strategies to encourage the development of more walkable (pedestrian friendly) and to improve accessibility to meet the needs of its users. This study details an analytical approach to measure 20-minute neighbourhoods with the aim of supporting city wide policies to identify opportunities to design more liveable 20-minute 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".