Examining the spatial and temporal relationship between social vulnerability and stay-at-home behaviors in New York City during the COVID-19 pandemic

Abstract

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.

Is compulsory home quarantine less effective than centralized quarantine in controlling the COVID-19 outbreak? Evidence from Hong Kong

“…Sociodemographic and healthcare factors, including population density and proportion of the elderly population aged 65 and above, as well as hospital beds and diabetes rates, are significant determinants of COVID-19 incidence rates ( Mansour et al, 2021 ). Furthermore, per capita income, population with disabilities, the proportion of the population aged 17 or below, poverty, automobile ownership, and educational level are found to significantly impact stay-at-home behavior in the U.S. ( Fu & Zhai, 2021 ). Living environment and housing quality can also be important predictors for the number of COVID-19 infections and death count (Hu et al, 2021; Das et al, 2021 ).…”

COVID-19 economic policy effects on consumer spending and foot traffic in the U.S.

“…Fu et al. [11] analyzed the spatial and temporal variations in stay-at-home behaviors against social vulnerability indicators, and highlighted the importance of understanding spatiotemporal pattern of public health behaviors in policy-making. Hu et al.…”

A longitudinal and geospatial analysis of COVID-19 tweets during the early outbreak period in the United States

“…However, in Los Angeles, Chicago, and Houston, clusters were mostly observed outside of city centers. Further research is needed to assess why these city and residential specific variations may be occurring, which could be influenced by differences in dynamics between working and living in different cities (including access to public transportation, commuting for work, and the impact of stay-athome orders on working and living conditions specific to cities or states) [35]. Areas that exhibited similar tweet clustering (e.g., the Phoenix area and New York City) require further study to assess if there are similar patterns of user risk-perception or COVID-19-related self-reported behavior.…”