Kummu Matti, Kosonen Maria, Masoumzadeh Sayyar Sina
Water and Development Research Group, Aalto University, Espoo, Finland.
Sci Data. 2025 Jan 30;12(1):178. doi: 10.1038/s41597-025-04487-x.
We present a comprehensive gridded GDP per capita dataset downscaled to the admin 2 level (43,501 units) covering 1990-2022. It updates existing outdated datasets, which use reported subnational data only up to 2010. Our dataset, which is based on reported subnational GDP per capita data from 89 countries and 2,708 administrative units, employs various novel methods for extrapolation and downscaling. Downscaling with machine learning algorithms showed high performance (R = 0.79 for cross-validation, R = 0.80 for the test dataset) and accuracy against reported datasets (Pearson R = 0.88). The dataset includes reported and downscaled annual data (1990-2022) for three administrative levels: 0 (national; reported data for 237 administrative units), 1 (provincial; reported data for 2,708 administrative units for 89 countries), and 2 (municipality; downscaled data for 43,501 administrative units). The dataset has a higher spatial resolution and wider temporal range than the existing data do and will thus contribute to global or regional spatial analyses such as socioenvironmental modelling and economic resilience evaluation. The data are available at https://doi.org/10.5281/zenodo.10976733 .
我们展示了一个全面的人均国内生产总值网格化数据集，该数据集已下推至二级行政区划（43,501个单元），涵盖1990 - 2022年。它更新了现有的过时数据集，那些数据集仅使用截至2010年的地方上报数据。我们的数据集基于89个国家和2,708个行政单位上报的地方人均国内生产总值数据，采用了各种新颖的外推和下推方法。使用机器学习算法进行下推显示出高性能（交叉验证的R = 0.79，测试数据集的R = 0.80），并且与上报数据集相比具有较高的准确性（皮尔逊R = 0.88）。该数据集包括三个行政级别（0级：国家；237个行政单位的上报数据）、1级（省级；89个国家2,708个行政单位的上报数据）和2级（市级；43,501个行政单位的下推数据）的上报和下推年度数据（1990 - 2022年）。该数据集具有比现有数据更高的空间分辨率和更宽的时间范围，因此将有助于进行全球或区域空间分析，如社会环境建模和经济复原力评估。数据可在https://doi.org/10.5281/zenodo.10976733获取。
