Fig 1.
Mobility indicators over time nationally and in the NYC DMA: 2020–2021.
Note: Mobility data was aggregated to the weekly-level for time-series analyses.
Fig 2.
Google Health Trends search volumes for economic stressor terms over time nationally and in the NYC DMA: 2020–2021.
Fig 3.
Google Health Trends search volumes for suicide seeking, mood and anxiety, and social stressor terms over time nationally and in the NYC DMA: 2020–2021.
Fig 4.
Heatmaps of cross-correlation coefficients for mobility indicators and Google Health Trends search volumes for economic stressor terms nationally and in the NYC DMA: 2020–2021.
P-values listed within cells. Note on interpretation: A cross-correlation coefficient at a negative weekly lag indicates that changes in the explanatory time series lead changes in the dependent time series that number of weeks later. A cross-correlation coefficient at a positive weekly lag indicates that changes in the dependent time series lead changes in the explanatory time series that number of weeks later. Correlations at the 0-week lag suggest that changes in both time series were concurrent. A positive cross-correlation coefficient indicates that there is a direct correlation between the time series and a negative cross-correlation coefficient indicates that there is an inverse correlation between the time series.
Fig 5.
Heatmaps of cross-correlation coefficients for mobility indicators and Google Health Trends search volumes for suicide seeking, mood and anxiety, and social stressor terms nationally and in the NYC DMA: 2020–2021.
P-values listed within cells. Note on interpretation: A cross-correlation coefficient at a negative weekly lag indicates that changes in the explanatory time series lead changes in the dependent time series that number of weeks later. A cross-correlation coefficient at a positive weekly lag indicates that changes in the dependent time series lead changes in the explanatory time series that number of weeks later. Correlations at the 0-week lag suggest that changes in both time series were concurrent. A positive cross-correlation coefficient indicates that there is a direct correlation between the time series and a negative cross-correlation coefficient indicates that there is an inverse correlation between the time series.