{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T21:58:57Z","timestamp":1791842337425,"version":"4.3.4"},"reference-count":47,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T00:00:00Z","timestamp":1678406400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Research and Development Program of Guangxi Province","award":["2019AB20003"],"award-info":[{"award-number":["2019AB20003"]}]},{"name":"Key Research and Development Program of Guangxi Province","award":["2019YFC1510601"],"award-info":[{"award-number":["2019YFC1510601"]}]},{"name":"Key Research and Development Program of Guangxi Province","award":["42071315"],"award-info":[{"award-number":["42071315"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019AB20003"],"award-info":[{"award-number":["2019AB20003"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFC1510601"],"award-info":[{"award-number":["2019YFC1510601"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["42071315"],"award-info":[{"award-number":["42071315"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2019AB20003"],"award-info":[{"award-number":["2019AB20003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2019YFC1510601"],"award-info":[{"award-number":["2019YFC1510601"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42071315"],"award-info":[{"award-number":["42071315"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Gridded gross domestic product (GDP) data are a crucial land surface parameter for many geoscience applications. Recently, machine learning approaches have become powerful tools in generating gridded GDP data. However, most machine learning approaches for gridded GDP estimation seldom consider the geographical properties of input variables. Therefore, in this study, a geographically weighted stacking ensemble learning approach was developed to generate gridded GDP data. Three algorithms\u2014random forest, XGBoost, and LightGBM\u2014were used as base models, and the linear regression in stacking ensemble learning was replaced by geographically weighted regression to locally fuse the three predictions. A case study was conducted in China to demonstrate the effectiveness of the proposed approach. The results showed that the proposed GDP downscaling approach outperformed the three base models and traditional stacking ensemble learning. Meanwhile, it had good predictive power on county-level GDP test data with R2 of 0.894, 0.976, and 0.976 for the primary, secondary, and tertiary sectors, respectively. Moreover, the predicted 1 km gridded GDP data had a high accuracy (R2 = 0.787) when evaluated by town-level GDP data. Hence, the proposed GDP downscaling approach provides a valuable option for generating gridded GDP data. The generated 1 km gridded GDP data of China from 2020 are of great significance for other applications.<\/jats:p>","DOI":"10.3390\/ijgi12030123","type":"journal-article","created":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T02:31:55Z","timestamp":1678674715000},"page":"123","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Generating Gridded Gross Domestic Product Data for China Using Geographically Weighted Ensemble Learning"],"prefix":"10.3390","volume":"12","author":[{"given":"Zekun","family":"Xu","sequence":"first","affiliation":[{"name":"College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Wang","sequence":"additional","affiliation":[{"name":"Flood and Drought Disaster Prevention and Protection Center of Heilongjiang Province, Haerbin 150006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guihou","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuehong","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Ma","sequence":"additional","affiliation":[{"name":"Research Center on Flood and Drought Disaster Reduction, China Institute of Water Resources and Hydropower Research, Beijing 100038, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoxiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"847","DOI":"10.5194\/essd-10-847-2018","article-title":"Continuous national gross domestic product (gdp) time series for 195 countries: Past observations (1850\u20132005) harmonized with future projections according to the shared socio-economic pathways (2006\u20132100)","volume":"10","author":"Geiger","year":"2018","journal-title":"Earth Syst. 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