We present hierarchical Bayesian methodology to perform spatio-temporal change of support (COS) for survey data with Gaussian sampling errors. This methodology is motivated by the American Community Survey (ACS), which is an ongoing survey administered by the U.S. Census Bureau that provides timely information on several key demographic variables. The ACS has published 1-year, 3-year, and 5-year period-estimates, and margins of errors, for demographic and socio-economic variables recorded over predefined geographies. The spatio-temporal COS methodology considered here provides data users with a way to estimate ACS variables on customized geographies and time periods, while accounting for sampling errors. Additionally, 3-year ACS period estimates are to be discontinued, and this methodology can provide predictions of ACS variables for 3-year periods given the available period estimates. The methodology is based on a spatio-temporal mixed effects model with a low-dimensional spatio-temporal basis function representation, which provides multi-resolution estimates through basis function aggregation in space and time. This methodology includes a novel parameterization that uses a target dynamical process and recently proposed parsimonious Moran's I propagator structures. Our approach is demonstrated through two applications using public-use ACS estimates, and is shown to produce good predictions on a holdout set of 3-year period estimates.
A Bayesian hierarchical small-area population model accounting for data source specific methodologies from American Community Survey, Population Estimates Program, and Decennial Census data
许多流行病学研究都需要小面积的人口估计，但其质量和准确性往往没有得到评估。在美国，美国人口普查局（USCB）以十年一次的人口普查（Decennial Census Counts）、两次人口普查之间的人口预测（Intercensal Population Projections，PEP）和美国社区调查（American Community Survey，ACS）的形式公布小区域的人口估计。虽然这些数据来源之间存在着重要的关系，但在数据收集和处理方法上存在着重要的对比，因此，每一套估计数可能会受到不同来源和误差幅度的影响。此外，由于每个数据源的调查后调整，这些数据源没有报告相同的小地区人口计数。由此产生的小地区疾病/死亡率可能会有所不同，具体取决于用于人口计数的数据源（分母数据）。为了准确地捕捉年度小面积人口计数和相关的不确定性，我们提出了一个贝叶斯人口模型（B-Pop），该模型融合了来自所有三个USCB来源的信息，考虑了数据源特定的方法和相关的误差。我们的框架的主要特点是：1）一个单一的模型集成多个数据源，2）占数据源特定的数据生成机制，并特别占数据源特定的错误，和3）预测的估计数年没有USCB报告的数据。我们的研究集中在佐治亚州的159个县，并产生2005-2021年的估计。
