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Social distancing and particularly staying at home are effective public health responses to the COVID-19 pandemic. The sheer scale of behavior changes across a mass population scale is unprecedented and will undoubtedly cause disproportionate hardships for certain vulnerable groups of population and marginalized communities during different periods of the pandemic. However, at the community level, few studies have considered the spatial and temporal variations in such public health behavior changes during this pandemic. We applied a geographically and temporally weighted regression (GTWR) to analyze the spatiotemporal pattern of community stay-at-home behaviors against social vulnerability indicators at the census tract level in New York City from March to August 2020. Our findings are generally supporting the conventional wisdom of social vulnerability yet they also offer new insights. Despite the spatial variations in the effects of social vulnerability on stay-at-home behaviors, people from different vulnerable groups are also exhibiting varying reactions to the pandemic over the duration of this study, thereby highlighting the importance of understanding the spatiotemporal pattern of public health behaviors to develop an effective policy response to avoid the risk of deepening inequalities and to promote a just and sustainable urban future.

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Amid sweeping efforts to get Americans to stay at home to slow the spread of the coronavirus disease, we geovisualized how foot traffic has increased or declined in relation to six types of trips across the United States: homes, workplaces, retail and recreation establishments, parks, grocery stores and pharmacies, and transit stations. The geovisualization shows that most West and East Coast cities have reduced extensive movements while many Middle American cities even increased their movements, such as trips to grocery stores and parks. We further found that the poorest communities reduced fewer movements than the wealthiest communities, except for the trips to parks.