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

TL;DR

This paper introduces a hierarchical Bayesian method for spatio-temporal change of support in survey data, enabling customized estimates over different geographies and periods while accounting for sampling errors, specifically applied to ACS data.

Contribution

The paper develops a novel spatio-temporal Bayesian framework with low-dimensional basis functions and a new parameterization for improved change of support in survey data.

Findings

Accurately predicts ACS variables for custom geographies and periods.

Effectively handles sampling errors in multi-year estimates.

Demonstrates good predictive performance on holdout data.

Abstract

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.