Riding through the pandemic: Using Strava data to monitor the impacts of COVID-19 on spatial patterns of bicycling
Bibliographic record
Abstract
COVID-19 prompted a bike boom and cities around the world responded to increased demand for space to ride with street reallocations. Evaluating these interventions has been limited by a lack of spatially-temporally continuous ridership data. Our paper aims to address this gap using crowdsourced data on bicycle ridership. We evaluate changes in spatial patterns of bicycling during the first wave of the COVID-19 pandemic (Apr - Oct 2020) in Vancouver, Canada using Strava data and a local indicator of spatial autocorrelation. We map statistically significant change in ridership and reference clusters of change to a high-resolution base map. Amongst streets where bicycling increased, we measured the proportion of increase occurring on pre-existing bicycle facilities or street reallocations compared to streets without. In all our analyses, we evaluate patterns across subsets of Strava data representing recreation, commuting, and ridership generated by women and older adults (55 + ). We found consistent and unique patterns by trip purpose and demographics: samples generated by women and older adults showed increases near green and blue spaces and on street reallocations that increased access to parks, and these patterns were also mirrored in the recreation sample. Commute ridership highlighted distinct patterns of increase around the hospital district. Across all subsets most increases occurred on bicycle facilities (pre-existing or provisional), with a strong preference for high-comfort facilities. We demonstrate that changes in spatial patterns of bicycle ridership can be monitored using Strava data, and that nuanced patterns can be identified using trip and demographic labels in the data.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".