Several global gridded population data sets are available at unprecedented high-resolution, including recent releases at 100-m, 30-m, and 10-m resolution. These data sets are the result of the application of advanced methods to disaggregate census population counts from administrative units and facilitated by the proliferation of increasingly high-resolution spatial information pertaining to the built environment (e.g. built-up and building footprint data). Accordingly, these gridded population data are increasingly dependent on a single ancillary data set to inform the distribution of populations across space. Our study tests several combinations of binary masking variables (land areas, all building footprints, residential building footprints) and density variables (building footprint areas, building volumes) derived from characteristics of the built environment at 20× and 8000× downscaling using a flexible equation for high-resolution global dasymetric population modeling. The assessment is applied in New York City, where large spatial heterogeneities exist across confined geographic areas. Results confirm that the performance of the model generally improves as: (i) the binary masking variable becomes increasingly limiting; and, (ii) the density variable becomes more pronounced. However, application requires careful consideration due to their propensity to amplify both positive results and errors.

可以以前所未有的高分辨率获得几个全球网格化人口数据集，包括最近发布的100-m，30-m和10-m分辨率的数据集。这些数据集是应用先进方法将人口普查人口数与行政单位进行分类的结果，并通过与建筑环境有关的高分辨率空间信息（例如，建筑和建筑足迹数据）的扩散而得到促进。因此，这些网格化的人口数据越来越依赖于单个辅助数据集来告知整个空间中的人口分布。我们的研究测试了二进制掩蔽变量（土地面积，所有建筑物的占地面积，住宅建筑物的占地面积）和密度变量（建筑物的占地面积，使用高分辨率方程式的高分辨率全局场人口模型，通过20倍和8000倍的缩小比例从建筑环境的特征中推导出来。该评估适用于纽约市，该纽约市在狭窄的地理区域内存在较大的空间异质性。结果证实该模型的性能通常会随着以下方面的改善：（i）二进制掩蔽变量变得越来越受限制；（ii）密度变量变得更明显。但是，由于应用程序倾向于放大正面结果和误差，因此需要仔细考虑。在有限的地理区域中存在较大的空间异质性的地方。结果证实该模型的性能通常会随着以下方面的改善：（i）二进制掩蔽变量变得越来越受限制；（ii）密度变量变得更明显。但是，由于应用程序倾向于放大正面结果和误差，因此需要仔细考虑。在有限的地理区域中存在较大的空间异质性的地方。结果证实该模型的性能通常会随着以下方面的改善：（i）二进制掩蔽变量变得越来越受限制；（ii）密度变量变得更明显。但是，由于应用程序倾向于放大正面结果和误差，因此需要仔细考虑。