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Activity Number: 249 - Bayesian Methods for Social and Human Data
Type: Contributed
Date/Time: Monday, July 30, 2018 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #329311 Presentation
Title: Bayesian Disaggregation of Spatio-Temporal Survey-Based Estimates: An Application to the American Community Survey
Author(s): Marco H. Benedetti* and Veronica J. Berrocal
Companies: University of Michigan and University of Michigan
Keywords: Spatio-temporal statistics; Areal unit data; Bayesian modeling; American Community Survey

The American Community Survey (ACS) is an ongoing survey administered by the US Census Bureau which collects social, economic, and other community data. ACS estimates are released annually, with varying spatial and temporal resolution: 5-year time periods refer to smaller municipal subdivisions, while 1-year time periods refer to larger areas. Although for epidemiological studies, these estimates contain important community information, their varying spatial and temporal resolution pose various challenges: the 5-year ACS estimates might be temporally misaligned with finely resolved health outcome data, conversely, the coarser 1-year estimates are likely spatially misaligned with finely resolved health data. In this paper, we present a Bayesian hierarchical model that leverages both 1-year and 5-year ACS data to obtain estimates of community characteristics at any given spatial and temporal resolution. The disaggregation is achieved by introducing a latent, point-referenced process, in turn modeled using a multi-resolution basis function expansion, which is linked to the ACS data via a stochastic model that is reflective of the survey design used to collect the data.

Authors who are presenting talks have a * after their name.

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