美国县到县的迁移建模:数据源和模型选择的影响
Philip E Morefield1,2, Timothy F Leslie1
1Department of Geography and Geoinformation Science, George Mason University, 4400 University Dr., MS 6C3, Fairfax, VA 22030, USA.
概括
准确地建模内部迁移是一个挑战. 这项研究比较了美国的数据集 (IRS,ACS,人口普查) 和模型,发现了数据准确性和人口统计细节之间的权衡,用于分析移民流.
科学领域:
- 人口统计学 人口统计学
- 经济地理 经济地理
- 计算社会科学 计算社会科学
背景情况:
- 内部移民对人口和经济模式产生重大影响.
- 准确地建模移民流动会带来持续的方法学挑战.
- 美国的数据集,如国税局,ACS和人口普查数据提供了洞察力,但有明显的局限性.
研究的目的:
- 评估不同美国数据源的各种迁移模型的性能.
- 评估数据源选择对迁移模型准确性和偏差的影响.
- 以数据特征为基础,为迁移建模提供最佳实践信息.
主要方法:
- 对三个美国数据源进行比较分析:国税局移民数据,美国社区调查 (ACS) 和人口普查长形式数据.
- 已建立的迁移模型的应用和评估:重力模型,Poisson回归和辐射模型.
- 评估框架侧重于预测错误,偏见,人口特异性和数据细节性.
主要成果:
- 美国国税局 (IRS) 的数据显示,汇总流量预测错误较低,但缺乏人口细节.
- 美国社区调查 (ACS) 和人口普查数据提供了更丰富的人口统计信息,并捕获了更多的移民流.
- 由于小流量估计和保密性抑制值,ACS和人口普查数据可能会引入噪音.
结论:
- 数据来源的选择对迁移建模结果产生了重大影响,需要与研究目标保持一致.
- 每个数据集都在汇总准确性和人口丰富性之间呈现独特的权衡.
- 结果有助于建立在移民研究中利用各种数据集的最佳实践.
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