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, https://doi.org/10.23719/1522948) and the EPA's EnviroAtlas (https://www.epa.gov/enviroatlas, last access: 15 June 2022; Pickard et al., 2015).

中文翻译：

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通过排除无人居住地区，改进美国本土 30 m 分辨率的智能测绘人口密度估计

人口变化几乎影响全球变化的方方面面，从土地利用、温室气体排放、生物多样性保护到疾病传播。人口密度空间模式的数据有助于我们了解人类住区的模式和驱动因素，并可以帮助我们量化我们面临的自然灾害、污染和传染病的风险。人口通常按形状和大小各不相同的国家或地区单位进行记录。使用这些不规则大小的单位和与人口动态相关的辅助数据，我们可以通过智能测绘（IDM）生成高分辨率的人口密度网格估计。网格化人口密度可以更详细地估计人口在较大单位内的分布情况。此外，我们可以通过指定对人口密度分析（例如我们对人类暴露的估计）有影响的无人居住地区来完善对人口密度的估计。在本研究中，我们使用各种地理空间数据集来扩展美国 (US) 环境保护局 (EPA) EnviroAtlas Dasymetric 人口地图中美国本土 (CONUS) 内无人居住地区的现有规范。与 EnviroAtlas dasymetric 人口地图现有的无人居住地区定义相比，我们发现 IDM 对美国人口普查局区块的人口估计在美国大陆所有州都有所改善。我们发现，IDM 在城市面积较大的州比在人口稀少的州表现更好。我们还更新了现有的 EnviroAtlas 智能 Dasymetric 绘图工具箱，并扩展了其接受无人居住区域的功能。更新后的美国大陆 30 m 人口密度可通过 EPA 的环境数据集网关（Baynes 等人，2021 年，https://doi.org/10.23719/1522948）和 EPA 的 EnviroAtlas（https://www.epa.gov/enviroatlas，最后访问日期：2022 年 6 月 15 日；皮卡德等人，2015）。