An Integrated Multi-Source Dataset for Measuring Settlement Evolution in the United States from 1810 to 2020.

Understanding changes in the built environment is vital for sustainable urban development and disaster preparedness. Recent years have seen the emergence of a variety of global, continent-level, and nation-wide datasets related to the current state and the evolution of the built environment, human settlements or building stocks. However, such datasets may face limitations like incomplete coverage, sparse building information, coarse resolution, and limited timeframes. This study addresses these challenges by integrating three spatial datasets to create an extensive, attribute-rich sequence of settlement layers spanning 200 years for the contiguous U.S. This integration process involves complex data processing, merging property-level real estate, parcel, and remote sensing-based building footprint data, and creating gridded multi-temporal settlement layers. This effort unveils the latest edition (Version 2) of the Historical Settlement Data Compilation for the U.S. (HISDAC-US), which includes the latest land use and structural information as of the year 2021. It enables detailed research on urban form and structure, helps assess and map the built environment's risk to natural hazards, assists in population modeling, supports land use analysis, and aids health studies.

Comparing Approximated Heat Stress Measures Across the United States.

Climate change is escalating the threat of heat stress to global public health, with the majority of humans today facing increasingly severe and prolonged heat waves. Accurate weather data reflecting the complexity of measuring heat stress is crucial for reducing the impact of extreme heat on health worldwide. Previous studies have employed Heat Index (HI) and Wet Bulb Globe Temperature (WBGT) metrics to understand extreme heat exposure, forming the basis for heat stress guidelines. However, systematic comparisons of meteorological and climate data sets used for these metrics and the related parameters, like air temperature, humidity, wind speed, and solar radiation crucial for human thermoregulation, are lacking. We compared three heat measures (HImax, WBGTBernard, and WBGTLiljegren) approximated from gridded weather data sets (ERA5-Land, PRISM, Daymet) with ground-based data, revealing strong agreement from HI and WBGTBernard (R 2 0.76-0.95, RMSE 1.69-6.64°C). Discrepancies varied by Köppen-Geiger climates (e.g., Adjusted R 2 HImax 0.88-0.95, WBGTBernard 0.79-0.97, and WBGTLiljegren 0.80-0.96), and metrological input variables (Adjusted R 2 T max 0.86-0.94, T min 0.91-0.94, Wind 0.33, Solarmax 0.38, Solaravg 0.38, relative humidity 0.51-0.74). Gridded data sets can offer reliable heat exposure assessment, but further research and local networks are vital to reduce measurement errors to fully enhance our understanding of how heat stress measures link to health outcomes.

Classifying Building Roof Damage Using High Resolution Imagery for Disaster Recovery.

Post-hurricane damage assessments are often costly and time-consuming. Remotely sensed data provides a complementary method of data collection that can be completed comparatively quickly and at relatively low cost. This study focuses on 15 Florida counties impacted by Hurricane Michael (2018), which had category 5 strength winds at landfall. The present study evaluates the ability of aerial imagery collected to cost-effectively measure blue tarps on buildings for disaster impact and recovery. A support vector machine model classified blue tarp, and parcels received a damage indicator based on the model's prediction. The model had an overall accuracy of 85.3% with a sensitivity of 74% and a specificity of 96.7%. The model results indicated approximately 7% of all parcels (27 926 residential and 4431 commercial parcels) in the study area as having blue tarp present. The study results may benefit jurisdictions that lacked financial resources to conduct on-the-ground damage assessments.

Disparities in COVID-19 health outcomes among different sub-immigrant groups in the US - a study based on the spatial Durbin model.

Immigrants may be more vulnerable to coronavirus disease 2019 (COVID-19) than other sub-population groups due to their relatively low socioeconomic status. However, no quantitative studies have examined the relationships between immigrants and COVID-19 health outcomes (confirmed cases and related deaths). We first examined the relationship between total immigrants and COVID-19 health outcomes with spatial Durbin models after controlling for demographic, biophysical and socioeconomic variables. We then repeated the same analysis within multiple subimmigrant groups divided by those with original nativity to examine the differential associations with health outcomes. The result showed that the proportion of all immigrants is negatively associated with the number of confirmed cases and related deaths. At the continent and sub-continent level, we consistently found negative relationships between the number of confirmed cases and the proportion of all sub-immigrant groups. However, we observed mixed associations between the proportion of sub-immigrant groups and the number of deaths. Those counties having a higher prevalence of immigrants from Africa [Eastern Africa: â€"18.6, 95% confidence interval (CI): â€"38.3~â€"2.9; Northern Africa: â€"146.5, 95% CI: â€"285.5~â€"20.1; Middle Africa: â€"622.6, 95% CI: â€"801.4~â€" 464.5] and the Americas (Northern America: â€"90.5, 95% CI: â€" 106.1~â€"73.8; Latin America: â€"6.8, 95% CI: â€"8.1~â€"5.2) mostly had a lower number of deaths, whereas those counties having a higher prevalence of immigrants from Asia (Eastern Asia: 21.0, 95% CI: 7.7~36.2; Western Asia: 42.5, 95% CI: 16.9~68.8; South- Central Asia: 26.6, 95% CI: 15.5~36.9) showed a higher number of deaths. Our results partially support that some immigrants, especially those from Asia, are more vulnerable to COVID-19 than other sub-population groups.

Verifying Experimental Wet Bulb Globe Temperature Hindcasts Across the United States.

Hot and humid heat exposures challenge the health of outdoor workers engaged in occupations such as construction, agriculture, first response, manufacturing, military, or resource extraction. Therefore, government institutes developed guidelines to prevent heat-related illnesses and death during high heat exposures. The guidelines use Wet Bulb Globe Temperature (WBGT), which integrates temperature, humidity, solar radiation, and wind speed. However, occupational heat exposure guidelines cannot be readily applied to outdoor work places due to limited WBGT validation studies. In recent years, institutions have started providing experimental WBGT forecasts. These experimental products are continually being refined and have been minimally validated with ground-based observations. This study evaluated a modified WBGT hindcast using the historical National Digital Forecast Database and the European Centre for Medium-Range Weather Forecasts Reanalysis v5. We verified the hindcasts with hourly WBGT estimated from ground-based weather observations. After controlling for geographic attributes and temporal trends, the average difference between the hindcast and in situ data varied from -0.64°C to 1.46°C for different Köppen-Geiger climate regions, and the average differences are reliable for decision making. However, the results showed statistically significant variances according to geographical features such as aspect, coastal proximity, land use, topographic position index, and Köppen-Geiger climate categories. The largest absolute difference was observed in the arid desert climates (1.46: 95% CI: 1.45, 1.47), including some parts of Nevada, Arizona, Colorado, and New Mexico. This research investigates geographic factors associated with systematic WBGT differences and points toward ways future forecasts may be statistically adjusted to improve accuracy.

Investigating city bike rental usage and wet-bulb globe temperature.

Extreme heat exacerbates human illness and constrains the intensity and/or duration of outdoor activities. Temperature is an incomplete metric of outdoor heat exposures. By contrast, Wet Bulb Globe Temperature (WBGT) is a heat exposure metric that considers air temperature, moisture, solar radiation, and wind speed. In the USA, bicycle activity increased by 60% from 2000 to 2010. Several studies examined weather conditions and bicycling activities. However, few studies examined how the public should conduct their daily physical activities per weather conditions.This study investigated the relationship between WBGT and shared city bicycle activity in New York City (NYC) and San Francisco (SF), USA. Generalized Additive Models examined nonlinear relationships between WBGT and bicycle activity while controlling for rider demographics and temporal trends. Next, bootstrapping estimated the "peak point", when the relationship between the bike rentals and WBGT notably changed.The analysis also examined whether the heat warning messages affected cycling activities. We found that the number of rented bikes declined at different peak points in each city. The peak point was in NYC at 34.3°C (95% CI 33°C-35°C) and 10.8°C (95% CI 10-12°C) in SF. Somewhat paradoxically, bike rentals increased when heat warnings were issued in both cities.