ABSTRACT
This study provided comparisons of confirmed cases between face-cover-required states and partially or not-required states from a time-series analysis on effects of face mask use in public based on eight different states between March 1 and June 15, 2020. In comparing face-cover-required states and partially or not-required states, it was very encouraging that the slope of the daily case trends turned negative after face-cover requirements in statewide face-cover-required states, including New York, New Jersey, Pennsylvania, and Connecticut. However, the patterns of the daily case have been showing positive trends continuously in partially or not-required states, including California, Texas, Florida, and Virginia. Based on our prediction model, if nationwide face-cover requirements with social distancing were enacted on March 16, the estimated number of deaths would be about 15,600, which is 94,300 less than the actual number of deaths by June 15, 2020. We recommend that all states and the federal government require face coverings in order to reduce the risk of infectious diseases.
ABSTRACT
Rapid urbanization has accelerated land use and land cover changes, and generated the urban heat island effect (UHI). Previous studies have reported positive effects of neighborhood landscapes on mitigating urban surface temperatures. However, the influence of neighborhood landscape spatial patterns on enhancing cooling effects has not yet been fully investigated. The main objective of this study was to assess the relationships between neighborhood landscape spatial patterns and land surface temperatures (LST) by using multi-regression models considering spatial autocorrelation issues. To measure the influence of neighborhood landscape spatial patterns on LST, this study analyzed neighborhood environments of 15,862 single-family houses in Austin, Texas, USA. Using aerial photos, geographic information systems (GIS), and remote sensing, FRAGSTATS was employed to calculate values of several landscape indices used to measure neighborhood landscape spatial patterns. After controlling for the spatial autocorrelation effect, results showed that larger and better-connected landscape spatial patterns were positively correlated with lower LST values in neighborhoods, while more fragmented and isolated neighborhood landscape patterns were negatively related to the reduction of LST.
