  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.
