Assessing downtown recovery rates and determinants in North American cities after the COVID-19 pandemic
Bibliographic record
Abstract
North American downtowns are struggling to recover from the global COVID-19 pandemic. This study aims to investigate the varying rates of recovery experienced by downtown areas in the 66 largest cities of the United States and Canada. Leveraging Location-Based Services data extracted from mobile phone location trajectories, we assess the recovery rates in the 2023 post-pandemic period, juxtaposed against pre-pandemic 2019 levels. We find significant disparities in downtown recovery rates. Economic factors emerge as crucial determinants, where downtowns hosting a concentration of sectors with remote/hybrid work options – such as information, finance, professional services and management – displayed sluggish recovery. Conversely, downtowns with a focus on industries like accommodation, manufacturing, education, retail, construction, entertainment and healthcare exhibited greater resilience post pandemic. Furthermore, higher density, crime rates and education levels were correlated with slower recovery rates, as were harsher weather conditions and longer commuting times. Lower-density and auto-orientated downtowns demonstrated a swift rebound, even surpassing pre-pandemic activity levels. These findings underscore the necessity for tailored policies to bolster the revival of North American downtown areas.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".