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We present hierarchical Bayesian methodology to perform spatio-temporalchange of support (COS) for survey data with Gaussian sampling errors. Thismethodology is motivated by the American Community Survey (ACS), which is anongoing survey administered by the U.S. Census Bureau that provides timelyinformation on several key demographic variables. The ACS has published 1-year,3-year, and 5-year period-estimates, and margins of errors, for demographic andsocio-economic variables recorded over predefined geographies. Thespatio-temporal COS methodology considered here provides data users with a wayto estimate ACS variables on customized geographies and time periods, whileaccounting for sampling errors. Additionally, 3-year ACS period estimates areto be discontinued, and this methodology can provide predictions of ACSvariables for 3-year periods given the available period estimates. Themethodology is based on a spatio-temporal mixed effects model with alow-dimensional spatio-temporal basis function representation, which providesmulti-resolution estimates through basis function aggregation in space andtime. This methodology includes a novel parameterization that uses a targetdynamical process and recently proposed parsimonious Moran's I propagatorstructures. Our approach is demonstrated through two applications usingpublic-use ACS estimates, and is shown to produce good predictions on a holdoutset of 3-year period estimates.

Spatio-Temporal Change of Support with Application to American Community Survey Multi-Year Period Estimates