|
Abstract:
|
Motivated by the American Community Survey (ACS; US Census), we present Bayesian methodology to perform spatio-temporal change of support (COS) for survey data with Gaussian sampling errors. 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 users a way to estimate ACS variables on customized geographies and time periods while accounting for sampling errors. Additionally, 3-year ACS period estimates will 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. The effectiveness of our approach is demonstrated through two applications using public-use ACS estimates and is shown to produce good predictions.
|
