使用公共数据来改进人口估计在一致的边界内
John R Logan1, Wenquan Zhang2, Zengwang Xu3
1Brown University.
概括
这项研究比较了邻里数据方法来跟踪随时间的变化. 使用小面积数据的基于特征 (TB) 方法在详细的人口普查数据可用时,比标准方法提高了准确性.
科学领域:
- 人口统计学 人口统计学
- 城市研究 城市研究
- 地理信息系统 地理信息系统
背景情况:
- 由于地理边界的变化,邻里变化研究需要互插数据.
- 标准的插值方法假定人口分布均,引入不准确性.
- 现有的方法在数据异质性和不同空间细粒度方面扎.
研究的目的:
- 为了评估标准纵向流域数据库 (LTDB) 估计与基于特征 (TB) 方法对邻近特征的准确性.
- 评估数据细节性和来源 (全计数与样本) 对估计准确性的影响.
- 确定基于特征的方法优于标准方法的条件.
主要方法:
- 通过使用2010年LTDB (标准) 和TB方法的边界,比较了2000年的社区特征.
- 利用小面积数据用于TB方法,以考虑空间异质性.
- 经过验证的估计与机密的,区块级原始人口普查数据相比.
主要成果:
- 基于特征的 (TB) 估计显著超过了区块层面 (例如种族,年龄,住房) 可用的变量LTDB估计.
- 当小面积数据具有采样可变性或空间细节较少时,TB方法的有效性会下降.
- 标准的LTDB方法存在局限性,因为它们假定人口分布均.
结论:
- 基于特征的方法在高分辨率,全数普查数据可访问时,为邻里变化估计提供了更高的准确性.
- 先进方法的有效性取决于可用的小面积数据的质量和细节性.
- 未来的研究应该专注于精炼方法,用于有限或以样本为基础的小区域数据的区域.
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