{"title":"采用多源高精度数据校正人口网格数据。","authors":"Shuang Wang, Chun Dong, Rong Zhao, Yu Zhang","doi":"10.1038/s41598-025-19249-3","DOIUrl":null,"url":null,"abstract":"<p><p>Population grid data provide a fundamental scientific basis for ecological planning and management, environmental monitoring and assessment, ecological early warning, and emergency response. Traditional population allocation methods based on administrative units frequently introduce cross-scale errors, and current population grid datasets require enhancement regarding regional fine-grained characteristics and population distribution. This study presents a methodology to correct public population grid data utilizing Multisource high precision data at the grid scale. WorldPop serves as the baseline data for correction. The original data undergoes enhancement through a new model, incorporating Multisource high precision auxiliary data. The results indicate that compared to the original data, the RMSE of the corrected population grid data decreased by more than seven times, with an R<sup>2</sup> of 0.997, demonstrating substantial improvement in data quality.</p>","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":"15 1","pages":"35614"},"PeriodicalIF":3.9000,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12518877/pdf/","citationCount":"0","resultStr":"{\"title\":\"Corrected population grid data using multisource high precision data.\",\"authors\":\"Shuang Wang, Chun Dong, Rong Zhao, Yu Zhang\",\"doi\":\"10.1038/s41598-025-19249-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Population grid data provide a fundamental scientific basis for ecological planning and management, environmental monitoring and assessment, ecological early warning, and emergency response. Traditional population allocation methods based on administrative units frequently introduce cross-scale errors, and current population grid datasets require enhancement regarding regional fine-grained characteristics and population distribution. This study presents a methodology to correct public population grid data utilizing Multisource high precision data at the grid scale. WorldPop serves as the baseline data for correction. The original data undergoes enhancement through a new model, incorporating Multisource high precision auxiliary data. The results indicate that compared to the original data, the RMSE of the corrected population grid data decreased by more than seven times, with an R<sup>2</sup> of 0.997, demonstrating substantial improvement in data quality.</p>\",\"PeriodicalId\":21811,\"journal\":{\"name\":\"Scientific Reports\",\"volume\":\"15 1\",\"pages\":\"35614\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2025-10-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12518877/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Scientific Reports\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1038/s41598-025-19249-3\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Reports","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41598-025-19249-3","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}
Corrected population grid data using multisource high precision data.
Population grid data provide a fundamental scientific basis for ecological planning and management, environmental monitoring and assessment, ecological early warning, and emergency response. Traditional population allocation methods based on administrative units frequently introduce cross-scale errors, and current population grid datasets require enhancement regarding regional fine-grained characteristics and population distribution. This study presents a methodology to correct public population grid data utilizing Multisource high precision data at the grid scale. WorldPop serves as the baseline data for correction. The original data undergoes enhancement through a new model, incorporating Multisource high precision auxiliary data. The results indicate that compared to the original data, the RMSE of the corrected population grid data decreased by more than seven times, with an R2 of 0.997, demonstrating substantial improvement in data quality.
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