Assessing Early Public Response to COVID-19-Related Restrictions in New York City Using Spatial Analysis of Urban Mobility Data
Bibliographic record
Abstract
The rapid spread of COVID-19 in the United States initiated shelter-in-place policies that significantly impacted human mobility and daily routines starting in March 2020. Prior literature has examined the differences in lockdown policy efficacy and compliance with government orders1-6, as well as the effect of mobility changes on case counts7-12. However, less attention has been placed on the connection between mobility and socio-demographics after the onset of COVID-19 within a city’s borders. This paper focused on how human mobility patterns in New York City during the first three months of the pandemic differed based on socio-demographic factors like age, household income, and method of transportation to work. A secondary analysis determined if the four measurements of mobility used, namely distance traveled from home, home dwell time, non-home dwell time, and percentage time home, yielded significantly different findings. A mobility ratio representing the change in mobility between the first two weeks of February and April 2020 was created using aggregated and anonymized cellphone mobility data from SafeGraph. A Global Moran’s Index was calculated for each mobility ratio to test for the presence of spatial autocorrelation, and then two spatial lag models were applied to account for the existence of autocorrelation. That there existed significant differences in mobility patterns based on socio-demographics reinforced the need for physical distancing policies that acknowledge the demographic diversity present not only between but also within cities.
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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 | 0.068 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.038 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".