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Full days worked at home account for 28 percent of paid workdays among Americans 20-64 years old, as of mid-2023. That's about four times the 2019 rate and ten times the rate in the mid-1990s. We first explain why the big shift to work from home has endured rather than reverting to prepandemic levels. We then consider how work-from-home rates vary by worker age, sex, education, parental status, industry and local population density, and why it is higher in the United States than other countries. We also discuss some implications for pay, productivity, and the pace of innovation. Over the next five years, US business executives anticipate modest increases in work-from-home rates at their own companies. Other factors that portend an enduring shift to work from home include the ongoing adaptation of managerial practices and further advances in technologies, products, and tools that support remote work. [ABSTRACT FROM AUTHOR]

Background: One issue in invasive plant ecology is identification of the factors related to the invasion process that increase number of non-native species. When invasion by non-native species increases, so does the probability that some non-native species will become harmful, or classified as invasive species, which disrupt natural ecosystems with attendant economic and social costs. I quantified patterns of how non-native species richness varied with vegetation types and human populations. To evaluate the relative importance of different predictor variables for invasion pathways in the conterminous United States, I modeled non-native plant species richness by county compared to current and historical human populations; greenhouses and nurseries; railroads, pipelines, transmission lines, and oil and gas wells; and land covers of impervious surface, development intensity categories, agriculture, and vegetation types. I also modeled these variables within vegetation types, excluding vegetation variables. Results: To summarize patterns, non-native plant species richness increased from 72 to 200 with increasing human population density classes. Forests and forest land use mosaics had the greatest mean number of non-native plant species, ranging from 121 to 166, whereas grasslands and grassland mosaics had the least number of non-native plant species, about 70. For modeling variable importance, all combined variables had R2 values of 56% (random forests regressor) and 54% (cubist regressor) for predictions of withheld observations of non-native plant species richness, with greenhouse density and percent forestlands as most influential variables. Single variables of greenhouses (R2 = 29%), historical and current human populations (R2 = 27% and 23%), impervious surface (25%), and medium intensity development (23%) were most associated with non-native plant species richness. For vegetation types, greenhouse and historical human population densities were influential variables particularly in forestlands, shrublands, and wetlands. Conclusions: Based on these models, human population measures and horticultural locations of greenhouses and plant nurseries may have stronger relationships than measures of land use disturbance and transport with non-native plant species richness. [ABSTRACT FROM AUTHOR]

The three decades from 1940 through 1970 mark a turning point in the spatial scale of Black–White residential segregation in the United States compared with earlier years. We decompose metropolitan segregation into three components: segregation within the city, within the suburbs, and between the city and its suburbs. We then show that extreme levels of segregation were well established in most cities by 1940, and they changed only modestly by 1970. In this period, changes in segregation were greater at the metropolitan scale, driven by racially selective population growth in the suburbs. We also examine major sources of rising segregation, including region, metropolitan total, and Black population sizes, and indicators of redlining in the central cities based on risk maps prepared by the Home Owners Loan Corporation (HOLC) in the late 1930s. In addition to overall regional differences, segregation between the city and suburbs and within suburbia increased more in metropolitan areas with larger Black populations, but this relationship was found only in the North. In contrast to some recent theorizing, there is no association between preparation of an HOLC risk map or the share of city neighborhoods that were redlined and subsequent change in any component of segregation. [ABSTRACT FROM AUTHOR]

Identifying relatively intact areas within ecosystems and determining the conditions favoring their existence is necessary for effective management in the context of widespread environmental degradation. In this study, we used 3766 surveys of randomly selected sites in the United States and U.S. Territories to identify the correlates of sites categorized as "oases" (defined as sites with relatively high total coral cover). We used occupancy models to evaluate the influence of 10 environmental predictors on the probability that an area (21.2‐km2 cell) would harbor coral oases defined at four spatial extents: cross‐basin, basin, region, and subregion. Across all four spatial extents, oases were more likely to occur in habitats with high light attenuation. The influence of the other environmental predictors on the probability of oasis occurrence were less consistent and varied with the scale of observation. Oases were most likely in areas of low human population density, but this effect was evident only at the cross‐basin and subregional extents. At the regional and subregional extents oases were more likely where sea‐surface temperature was more variable, whereas at the larger spatial extents the opposite was true. By identifying the correlates of oasis occurrence, the model can inform the prioritization of reef areas for management. Areas with biophysical conditions that confer corals with physiological resilience, as well as limited human impacts, likely support coral reef oases across spatial extents. Our approach is widely applicable to the development of conservation strategies to protect biodiversity and ecosystems in an era of magnified human disturbance. [ABSTRACT FROM AUTHOR]

Recent literature has suggested a link between poor air quality and worse COVID-19 outcomes. In the United States, this link is particularly noteworthy because of residential sorting along ethnic lines within the US population; minorities are disproportionately exposed to health hazards, including air pollution. The impacts of the COVID-19 pandemic have also been disproportionately concentrated amongst minorities. We explore the association between air quality and COVID-19 outcomes, using county level data for the United States from the first wave of the pandemic in 2020, and test whether exposure to more polluted air can account for some of the observed disparities in COVID-19 outcomes among minorities. [ABSTRACT FROM AUTHOR]

Population change impacts almost every aspect of global change from land use, to greenhouse gas emissions, to biodiversity conservation, to the spread of disease. Data on spatial patterns of population density help us understand patterns and drivers of human settlement and can help us quantify the exposure we face to natural disasters, pollution, and infectious disease. Human populations are typically recorded by national or regional units that can vary in shape and size. Using these irregularly sized units and ancillary data related to population dynamics, we can produce high-resolution gridded estimates of population density through intelligent dasymetric mapping (IDM). The gridded population density provides a more detailed estimate of how the population is distributed within larger units. Furthermore, we can refine our estimates of population density by specifying uninhabited areas which have impacts on the analysis of population density such as our estimates of human exposure. In this study, we used various geospatial datasets to expand the existing specification of uninhabited areas within the United States (US) Environmental Protection Agency's (EPA) EnviroAtlas Dasymetric Population Map for the conterminous United States (CONUS). When compared to the existing definition of uninhabited areas for the EnviroAtlas dasymetric population map, we found that IDM's population estimates for the US Census Bureau blocks improved across all states in the CONUS. We found that IDM performed better in states with larger urban areas than in states that are sparsely populated. We also updated the existing EnviroAtlas Intelligent Dasymetric Mapping toolbox and expanded its capabilities to accept uninhabited areas. The updated 30 m population density for the CONUS is available via the EPA's Environmental Dataset Gateway (Baynes et al., 2021, 10.23719/1522948) and the EPA's EnviroAtlas (https://www.epa.gov/enviroatlas , last access: 15 June 2022; Pickard et al., 2015). [ABSTRACT FROM AUTHOR]

Background The United States Centers for Disease Control and Prevention (CDC)-sanctioned prevention strategies have included frequent handwashing with soap and water, covering the mouth and nose with a mask when around others, cleaning and disinfecting maintaining a distance of at least 6 feet from others, etc. Although many of these recommendations are based upon observation and past infection control practices, it is important to combine and explore public data sets to identify predictors of infection, morbidity and mortality to develop more finely honed interventions, based on sociodemographic factors. Method Cross-sectional study of both states in the US and counties in NY state. Results Population density was found to be significantly associated with state-level coronavirus infection and mortality rate (b  = 0.49, 95% confidence interval (CI): 0.34, 0.64, P  < .0001). States that have lower socioeconomic status, lower mean age and denser populations are associated with higher incidence rates. In regard to NY state, counties with a higher percentage of minority residents had higher COVID-19 mortality rates (b  = 2.61, 95% CI: 0.36, 4.87, P  = 0.023). Larger population cohorts were associated with lower COVID-19 mortality rates after adjusting for other variables in the model (b  = −1.39, 95% CI: −2.07, −0.71, P  < 0.001). Population density was not significantly associated with COVID-19 mortality rates after adjustment across counties in the NY state. Public ridership was not indicative of cases or mortality across states in the USA; however, it is a significant factor associated with incidence (but not mortality) in NY counties. Conclusion Population density was the only significant predictor of mortality across states in the USA. Lower mean age, lower median household incomes and more densely populated states were at higher risk of COVID-19 infection. Population density was not found to be a significant independent variable compared to minority status and socioeconomic factors in the New York epicenter. Meanwhile, public ridership was found to be a significant factor associated with incidence in New York counties. [ABSTRACT FROM AUTHOR]