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Activity Number: 335 - SRMS/SSS/GSS Student Paper Competition
Type: Topic Contributed
Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
Sponsor: Social Statistics Section
Abstract #304709 Presentation
Title: Accounting for Survey Design in Bayesian Disaggregation of Survey-Based Areal Estimates of Proportions
Author(s): Marco Benedetti* and Veronica J. Berrocal
Companies: University of Michigan and University of Michigan
Keywords: Spatio-temporal change of support problem; spatio-temporal statistics; American Community Survey; Survey estimates; Bayesian hierarchical model

Understanding the effects of neighborhood change on health requires data on characteristics of the neighborhoods in which subjects live. However, estimates of these characteristics are often aggregated over space and time in a fashion that diminishes their utility. Take, for example, estimates derived from the American Community Survey (ACS), in which estimates for small municipal areas are aggregated over 5-year periods, while 1-year estimates are only available for municipal areas with populations >65,000. Researchers may wish to use ACS estimates in studies of population health to characterize neighborhood-level exposures. However, 5-year estimates may not properly characterize temporal changes or align temporally with other data in the study, while the coarse spatial resolution of the 1-year estimates diminishes their utility in characterizing neighborhood exposure. To circumvent this issue, we propose a modeling framework to disaggregate estimates of proportions derived from sampling surveys, and account for the survey design effect. Application to ACS estimates of poverty and race demonstrate its utility in disaggregating these estimates to a fine spatio-temporal resolution.

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

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