Transit-driven resilience: Unraveling post-COVID-19 urban recovery dynamics.
Bibliographic record
Abstract
Transit-oriented communities (TOCs), characterized by compact, walkable designs and convenient access to public transportation, have historically been inclusive, resilient, and desirable places to reside. The global COVID-19 pandemic disrupted established transportation patterns, reshaping neighborhood activity and potentially altering the appeal of TOCs. This study examines the resilience of neighborhoods surrounding subway stations in Toronto post-pandemic, comparing their recovery trajectories with control neighborhoods and exploring associated factors. Using regression model and leveraging location-based services (LBS) data from mobile phones, we assess activity levels in 2023 relative to pre-pandemic levels in 2019. Our findings suggest that, despite ongoing ridership challenges, neighborhoods near transit stations in Toronto exhibited faster recovery than those farther away, reflecting associations with transit proximity, land-use diversity, and socio-economic characteristics. The presence of industries such as accommodation, food services, arts, entertainment, healthcare, and education near transit stations was linked to a diverse economic landscape, potentially sustaining vibrant commercial areas amid shocks. Conversely, neighborhoods with a higher prevalence of workplaces for sectors with remote work potential showed slower recovery. Variables such as proximity to jobs and essential amenities, walkability, and shorter commute times were also strongly associated with higher recovery rates. These findings offer actionable insights for policymakers and urban planners, highlighting the importance of integrating public transit with diverse land uses, socio-economic attributes, and equitable urban policies to support sustainable and resilient neighborhoods in the face of future crises.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".