  We introduce Bayesian spatial change of support methodology for count-valued
survey data with known survey variances. Our proposed methodology is motivated
by the American Community Survey (ACS), an ongoing survey administered by the
U.S. Census Bureau that provides timely information on several key demographic
variables. Specifically, the ACS produces 1-year, 3-year, and 5-year
"period-estimates," and corresponding margins of errors, for published
demographic and socio-economic variables recorded over predefined geographies
within the United States. Despite the availability of these predefined
geographies it is often of interest to data users to specify customized
user-defined spatial supports. In particular, it is useful to estimate
demographic variables defined on "new" spatial supports in "real-time." This
problem is known as spatial change of support (COS), which is typically
performed under the assumption that the data follows a Gaussian distribution.
However, count-valued survey data is naturally non-Gaussian and, hence, we
consider modeling these data using a Poisson distribution. Additionally,
survey-data are often accompanied by estimates of error, which we incorporate
into our analysis. We interpret Poisson count-valued data in small areas as an
aggregation of events from a spatial point process. This approach provides us
with the flexibility necessary to allow ACS users to consider a variety of
spatial supports in "real-time." We demonstrate the effectiveness of our
approach through a simulated example as well as through an analysis using
public-use ACS data.
