Spatiotemporal non-stationarity in green space and stress relationships: Recent evidence from South Korea.

A growing body of literature has linked exposure to "green space" (vegetation-rich areas) and other forms of nature to mental health. Exposure-outcome associations at regional or national scales can overlook local associations that define how geographically distinct populations may experience nature differently. Large-scale results might downplay the importance of lived experiences and heterogeneity of human-nature relationships at local scales. The current study examines three types of vegetative cover and identifies how they are associated with perceived stress in South Korea during and before the COVID-19 pandemic. We find forest cover is consistently negatively associated with perceived stress at nationwide scales. In contrast, grass cover and the normalized difference vegetation index (NDVI) show mixed associations with perceived stress at nationwide scales. Models accounting for spatial and temporal variability demonstrate that associations of forest cover, grass cover, and NDVI with perceived stress varies across the country and the study's four-year time horizon. Local governments may need divergent urban greening strategies for health promotion that respond to their specific sociodemographic and pre-existing environmental conditions.

COVID-19 Deaths in the United States: Shifts in Hot Spots over the Three Phases of the Pandemic and the Spatiotemporally Varying Impact of Pandemic Vulnerability.

The geographic areas most impacted by COVID-19 may not remain static because public health measures/behaviors change dynamically, and the impacts of pandemic vulnerability also may vary geographically and temporally. The nature of the pandemic makes spatiotemporal methods essential to understanding the distribution of COVID-19 deaths and developing interventions. This study examines the spatiotemporal trends in COVID-19 death rates in the United States from March 2020 to May 2021 by performing an emerging hot spot analysis (EHSA). It then investigates the effects of the COVID-19 time-dependent and basic social vulnerability factors on COVID-19 death rates using geographically and temporally weighted regression (GTWR). The EHSA results demonstrate that over the three phases of the pandemic (first wave, second wave, and post-vaccine deployment), hot spots have shifted from densely populated cities and the states with a high percentage of socially vulnerable individuals to the states with relatively relaxed social distancing requirements, and then to the states with low vaccination rates. The GTWR results suggest that local infection and testing rates, social distancing interventions, and other social, environmental, and health risk factors show significant associations with COVID-19 death rates, but these associations vary over time and space. These findings can inform public health planning.