National Socio-Environmental Synthesis Center, Annapolis, MD, 21401, USA.
Rubenstein School of Environment and Natural Resources, University of Vermont, Burlington, VT, 05405, USA.
Sci Data. 2022 Aug 27;9(1):523. doi: 10.1038/s41597-022-01603-z.
Assessment of socio-environmental problems and the search for solutions often require intersecting geospatial data on environmental factors and human population densities. In the United States, Census data is the most common source for information on population. However, timely acquisition of such data at sufficient spatial resolution can be problematic, especially in cases where the analysis area spans urban-rural gradients. With this data release, we provide a 30-m resolution population estimate for the contiguous United States. The workflow dasymetrically distributes Census block level population estimates across all non-transportation impervious surfaces within each Census block. The methodology is updatable using the most recent Census data and remote sensing-based observations of impervious surface area. The dataset, known as the U.G.L.I (updatable gridded lightweight impervious) population dataset, compares favorably against other population data sources, and provides a useful balance between resolution and complexity.
评估社会环境问题并寻找解决方案通常需要交叉使用环境因素和人口密度的地理空间数据。在美国，人口普查数据是人口信息的最常见来源。然而，在分析区域跨越城乡梯度的情况下，及时获取具有足够空间分辨率的此类数据可能会有问题。通过本次数据发布，我们提供了美国大陆地区的 30 米分辨率人口估计值。工作流程将人口普查块级的人口估计值以面状形式分配到每个人口普查块内的所有非交通不可渗透表面上。该方法可以使用最新的人口普查数据和基于遥感的不可渗透表面面积观测值进行更新。该数据集称为 U.G.L.I（可更新的网格化轻量级不可渗透）人口数据集，与其他人口数据源相比具有优势，并在分辨率和复杂性之间提供了很好的平衡。
