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Downscaled gridded global dataset for gross domestic product (GDP) per capita PPP over 1990–2022
by
Kummu, Matti
, Kosonen, Maria
, Masoumzadeh Sayyar, Sina
in
706/2808
/ 706/689/159
/ Data Descriptor
/ Datasets
/ Humanities and Social Sciences
/ Machine learning
/ multidisciplinary
/ Per capita
/ Science
/ Science (multidisciplinary)
/ Spatial discrimination
2025
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Downscaled gridded global dataset for gross domestic product (GDP) per capita PPP over 1990–2022
by
Kummu, Matti
, Kosonen, Maria
, Masoumzadeh Sayyar, Sina
in
706/2808
/ 706/689/159
/ Data Descriptor
/ Datasets
/ Humanities and Social Sciences
/ Machine learning
/ multidisciplinary
/ Per capita
/ Science
/ Science (multidisciplinary)
/ Spatial discrimination
2025
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Do you wish to request the book?
Downscaled gridded global dataset for gross domestic product (GDP) per capita PPP over 1990–2022
by
Kummu, Matti
, Kosonen, Maria
, Masoumzadeh Sayyar, Sina
in
706/2808
/ 706/689/159
/ Data Descriptor
/ Datasets
/ Humanities and Social Sciences
/ Machine learning
/ multidisciplinary
/ Per capita
/ Science
/ Science (multidisciplinary)
/ Spatial discrimination
2025
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Downscaled gridded global dataset for gross domestic product (GDP) per capita PPP over 1990–2022
Journal Article
Downscaled gridded global dataset for gross domestic product (GDP) per capita PPP over 1990–2022
2025
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Overview
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 It updates existing outdated datasets, which use reported subnational data only up to 2010.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
2
= 0.79 for cross-validation, R
2
= 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
.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
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