Assessing the applicability of the 15-minute city: Insights from a spatial accessibility perspective
Bibliographic record
Abstract
The concept of a 15-minute city proposes that residents should have access to all basic services within a short walking distance from their homes. However, most previous studies have focused on macro-level urban planning or land use configurations, while fine-grained, community-level evaluations that integrate actual travel constraints such as road network structures and the spatial distribution of service facilities remain underdeveloped. To explore the current state of the 15-minute city’s implementation at the community level and identify potential improvements, this study takes City of Toronto as an empirical case, and incorporates road network-based isochrones into an improved two-step floating catchment area model to calculate the spatial distribution of accessibility. A geographically weighted regression (GWR) model is used to analyze the impact of road network structure and the number of facilities on accessibility. The analysis results indicate that current urban infrastructure cannot meet the travel demands of the 15-minute city, particularly for walking. In the case of other “x-minute cities,” extending the travel time threshold is associated with improved accessibility in certain urban areas, but these benefits are limited to regions around service hubs, while accessibility in other areas tends to show a decrease in accessibility. This study offers recommendations for improving the 15-minute accessibility. Namely, if policymakers aim to encourage more residents to meet their daily needs within a 15-minute radius, a targeted increase in the number of facilities in specific areas is necessary. This is particularly crucial for pedestrians in suburban areas, where adding more facilities is essential to enhance accessibility. Lastly, in areas where facilities are lacking, the benefits of solely promoting walkable communities are limited to the urban environment, and encouraging cycling could be a more effective strategy.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".