Downscaled gridded global dataset for gross domestic product (GDP) per capita PPP over 1990-2022.

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-01-30 DOI:10.1038/s41597-025-04487-x
Matti Kummu, Maria Kosonen, Sina Masoumzadeh Sayyar
{"title":"Downscaled gridded global dataset for gross domestic product (GDP) per capita PPP over 1990-2022.","authors":"Matti Kummu, Maria Kosonen, Sina Masoumzadeh Sayyar","doi":"10.1038/s41597-025-04487-x","DOIUrl":null,"url":null,"abstract":"<p><p>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 It updates existing outdated datasets, which use reported subnational data only up to 2010.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<sup>2</sup> = 0.79 for cross-validation, R<sup>2</sup> = 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 .</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"178"},"PeriodicalIF":6.9000,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11782586/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-025-04487-x","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

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 (R2 = 0.79 for cross-validation, R2 = 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 .

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
1990-2022年人均国内生产总值(GDP)网格化全球数据集。
我们提出了一个全面的网格化人均GDP数据集,该数据集缩小到行政2级(43,501个单位),涵盖1990-2022年。它更新了现有的过时的数据集,这些数据集仅使用截至2010年的地方报告数据。我们的数据集基于来自89个国家和2,708个行政单位的地方人均GDP数据,采用了各种新颖的外推和缩减方法。使用机器学习算法降尺度显示出高性能(交叉验证R2 = 0.79,测试数据集R2 = 0.80)和对报告数据集的准确性(Pearson R = 0.88)。该数据集包括三个行政级别的报告和缩减的年度数据(1990-2022):报告数据237个行政单位),1个(省;报告数据来自89个国家的2,708个行政单位)和2个(直辖市;缩小了43,501个行政单位的数据)。该数据集比现有数据具有更高的空间分辨率和更宽的时间范围,因此将有助于全球或区域空间分析,如社会环境建模和经济复原力评估。相关数据可从https://doi.org/10.5281/zenodo.10976733获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
期刊最新文献
A telomere-to-telomere reference genome assembly of the Hypomesus nipponensis. Chromosome-level assembly of spotted steed (Hemibarbus maculatus Bleeker, 1871) genome. A dataset of insect sounds from 459 species for bioacoustic machine learning. A digital biomarker dataset from hematopoietic cell transplant caregivers and patients. Chromosome-level genomes of hard clams Meretrix lamarckii (Deshayes, 1853) and Meretrix meretrix (Linnaeus, 1758).
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1