Can we save the downtown? Examining pandemic recovery trajectories across 62 North American cities
Bibliographic record
Abstract
As cities emerge from the COVID-19 pandemic, the persistence of pandemic-era habits such as remote and hybrid work remained ingrained in urban activity patterns, presenting a threat to North American downtown districts as we know them. This paper examines the visitation trajectories of downtowns in 62 of the largest US and Canadian cities between 2020 and 2022 using location-based services data from mobile phones . Our analysis shows that downtowns with high concentrations of professional services, information, and finance fields, high density, long commute times, and colder winter temperatures continually struggle to maintain both raw visitation numbers and overall visitation proportions throughout the analysis period. In contrast, downtowns with higher concentrations of industries like healthcare, education, arts & entertainment, and public administration recovered well, and in some cases exceeded their pre-pandemic visitation performance. We also found that the length of COVID-19 restrictions and pre-pandemic amount or characteristics of housing had lesser correlations with overall downtown recovery trajectories, suggesting the economic structure and environment had greater influence. We hope this analysis can inform city governments, downtown business associations, real estate developers, and communities on how to reinvent the North American downtown in order to remain the apexes of urban activity in the post-pandemic era.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".