Improving intelligent dasymetric mapping population density estimates at 30 m resolution for the conterminous United States by excluding uninhabited areas

Federally-overlooked flood risk inequities in the conterminous United States

Climate change is exacerbating flood risks globally. In the U.S., Federal Emergency Management Agency (FEMA) Flood Insurance Rate Maps (FIRMs) delineate areas at high flood risk (i.e., 100-year floodplains), however, FIRMs are incomplete and outdated. We utilize risk estimates from advanced flood modeling and sociodemographic data at the census tract level to examine inequities in risk to federally-overlooked 100-year flooding across the conterminous United States (CONUS). Using multivariable statistics that account for clustering and non-normality, we analyze inequities by flood type (coastal, pluvial, and fluvial) and along the urban-rural continuum. Results indicate that 26 million CONUS residents live in federally-overlooked 100-year floodplains. Lower income is associated with elevated risk of federally-overlooked 100-year flooding for all flood types across the urban-rural continuum. We find inconsistent relationships for neighborhood racial/ethnic composition, yet increased Black composition is associated with greater risk of federally-overlooked 100-year pluvial flooding in metropolitan areas, where 47% of the CONUS population at risk to federally-overlooked flooding resides. In suburban and rural contexts, higher Hispanic/Latinx composition is associated with increased risk of federally-overlooked fluvial flooding. The large scale and inequities of federally-overlooked flood risk we find have major public policy, planning and environmental justice implications that demand flood resilience interventions.

Monthly electricity consumption data at 1 km × 1 km grid for 280 cities in China from 2012 to 2019

High spatio-temporal resolution estimates of electricity consumption are essential for formulating effective energy transition strategies. However, the data availability is limited by complex spatio-temporal heterogeneity and insufficient multi-source feature fusion. To address these issues, this study introduces an innovative downscaling method that combines multi-source data with machine learning and spatial interpolation techniques. The method's accuracy showed significant improvements, with determination coefficients (R2) increasing by 30.1% and 33.4% over the baseline model in two evaluation datasets. With this advanced model, we estimated monthly electricity consumption across 1 km x 1 km grid for 280 Chinese cities from 2012 to 2019. Our dataset is highly consistent with officially released electricity consumption of different industries (Pearson correlation coefficients within 0.83 - 0.91). Moreover, our data can reflect the electricity consumption patterns of different urban land uses compared to other datasets. This study bridges a significant gap in fine-grained electricity consumption data, providing a robust foundation for the development of sustainable energy policies.

Using POI and multisource satellite datasets for mainland China's population spatialization and spatiotemporal changes based on regional heterogeneity

Geospatial big data and remote sensing data are widely used in population spatialization studies. However, the relationship between them and population distribution has regional heterogeneity in different geographic contexts. It is necessary to improve data processing methods and spatialization models in areas with large geographical differences. We used land cover data to extract human activity, nighttime light and point-of-interest (POI) data to represent human activity intensity, and considered differences in geographical context to divide mainland China into northern, southern and western regions. We constructed random forest models to generate gridded population distribution datasets with a resolution of 500 m, and quantitatively evaluated the importance of auxiliary data in different geographical contexts. The street-level accuracy assessment showed that our population dataset is more accurate than WorldPop, with a higher R2 and smaller deviation. The improved datasets provided broad potential for exploring the spatial-temporal changes in grid-level population distribution in China from 2010 to 2020. The results indicated that the population density and settlement area have increased, and the overall pattern of population distribution has remained highly stable, but there are significant differences in population change patterns among cities with different urbanization processes. The importance of the ancillary data to the models varied significantly, with POI contributing the most to the southern region and the least to the western region. Moreover, POI had relatively less influence on model improvement in undeveloped areas. Our study could provide a reference for predicting social and economic spatialized data in different geographical context areas using POI and multisource satellite data.