美国国家合成人口数据集
James Rineer1, Nicholas Kruskamp2, Caroline Kery2
1RTI International, 3040 Cornwallis Rd., P.O. Box 12194, Research Triangle Park, NC, 27709, USA. jrin@rti.org.
Scientific data
|January 25, 2025
概括
研究人员在2019年为美国生成了合成人口数据,使得社区级研究成为可能. 这种空间显式数据集准确地代表了数百万户家庭和个人,验证了具有高相关性的人口普查数据.
科学领域:
- 计算社会科学 计算社会科学
- 人口模型的人口模型
- 地理信息系统 (GIS) 是指地理信息系统.
背景情况:
- 准确,空间显式的人口数据对于研究社区层面的影响至关重要.
- 传统的方法往往缺乏详细的邻里分析所需的细节性.
- 合成数据生成为创建统计学上强大,地理上精确的数据集提供了解决方案.
研究的目的:
- 为了展示一个工作流来生成美国的空间显式合成群体.
- 创建一个数据集,以颗粒级别代表2019年的美国家庭和个人.
- 通过合成数据,实现对社区和社区动态的新研究.
主要方法:
- 使用了美国人口普查美国社区调查 (ACS) 的5年估计 (2015-2019).
- 雇佣的代比例拟合 (IPF) 来生成合成的人口计数.
- 用环境保护局 (EPA) 综合气候和土地利用情景 (ICLUS) 数据进行空间分配的家庭.
主要成果:
- 创建了一个合成数据集,包含超过1.2亿个家庭和3.03亿个个人.
- 通过整合家庭分布估计,创建了一个空间显式数据集.
- 对原始人口普查变量实现了强有力的验证,皮尔森的r相关系数经常超过0.99.
结论:
- 证明的工作流成功地产生了统计准确和空间显式的合成人口数据.
- 这种方法为研究人员研究邻里级现象提供了宝贵的资源.
- 与人口普查数据的高相关性证实了生成的合成人口的可靠性和实用性.
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