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2000年至2022年LandScan全球30弧秒年度全球网格化人口数据集。

LandScan Global 30 Arcsecond Annual Global Gridded Population Datasets from 2000 to 2022.

作者信息

Lebakula Viswadeep, Sims Kelly, Reith Andrew, Rose Amy, McKee Jake, Coleman Phil, Kaufman Jason, Urban Marie, Jochem Chris, Whitlock Carrie, Ogden Mitchell, Pyle Joe, Roddy Darrell, Epting Justin, Bright Eddie

机构信息

Oak Ridge National Laboratory, Oak Ridge, USA.

出版信息

Sci Data. 2025 Mar 24;12(1):495. doi: 10.1038/s41597-025-04817-z.

Abstract

Oak Ridge National Laboratory (ORNL) annually develops the LandScan Global (LSG) dataset, a 30 arcsecond global gridded population dataset representing global ambient human population distribution. This multivariable dasymetric model disaggregates census counts within administrative boundaries using ancillary data. Each country's distribution reflects cultural and socioeconomic patterns; manual validations yield a unique global dataset for assessing populations at risk. For over two decades, LSG has been a standard for estimating populations at risk, aiding U.S. federal government, academia and humanitarian organizations. During disasters such as the 2004 Indian Ocean tsunami and the 2010 Haiti earthquake and geopolitical crises such as the Syrian civil war and the 2022 Russian invasion of Ukraine, LSG supported scientific and operational communities in emergency response and recovery. In 2022, LSG datasets from 2000 onward were made publicly available through ORNL's LandScan Portal. This data descriptor details our methodology and the application of geospatial science and machine learning to geographic and demographic data, highlighting uses in urban resiliency, emergency management, disaster response, and human health and security.

摘要

橡树岭国家实验室(ORNL)每年都会开发全球土地扫描(LSG)数据集,这是一个30弧秒的全球网格化人口数据集,代表全球环境人口分布。这种多变量区域加权模型利用辅助数据在行政边界内分解人口普查计数。每个国家的分布反映了文化和社会经济模式;人工验证产生了一个独特的全球数据集,用于评估风险人群。二十多年来,LSG一直是估计风险人群的标准,为美国联邦政府、学术界和人道主义组织提供帮助。在2004年印度洋海啸和2010年海地地震等灾害以及叙利亚内战和2022年俄罗斯入侵乌克兰等地缘政治危机期间,LSG支持了应急响应和恢复方面的科学和运营团体。2022年,2000年起的LSG数据集通过ORNL的土地扫描门户公开提供。本数据描述详细介绍了我们的方法以及地理空间科学和机器学习在地理和人口数据中的应用,重点介绍了在城市韧性、应急管理、灾害响应以及人类健康与安全方面的用途。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84c3/11933676/96d8c8bf1982/41597_2025_4817_Fig1_HTML.jpg

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