Bayesian Spatial Change of Support for Count-Valued Survey Data

TL;DR

This paper develops a Bayesian spatial change of support method tailored for count data from surveys like the ACS, enabling real-time, user-defined spatial aggregations while accounting for survey errors and non-Gaussian data.

Contribution

It introduces a novel Bayesian Poisson-based approach for spatial change of support that handles count data and survey variances, improving flexibility over Gaussian assumptions.

Findings

Method effectively estimates counts on new spatial supports.

Demonstrated success with simulated and ACS data.

Allows real-time, customized spatial analysis.

Abstract

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.