TY - JOUR T1 - Bayesian Spatial Change of Support for Count-Valued Survey Data with Application to the American Community Survey JF - Journal of the American Statistical Association Y1 - 2015 A1 - Bradley, Jonathan R. A1 - Wikle, Christopher K. A1 - Holan, Scott H. KW - Aggregation KW - American Community Survey KW - Bayesian hierarchical model KW - Givens angle prior KW - Markov chain Monte Carlo KW - Multiscale model KW - Non-Gaussian. AB - 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 show the effectiveness of our approach through a simulated example as well as through an analysis using public-use ACS data. UR - http://www.tandfonline.com/doi/abs/10.1080/01621459.2015.1117471 ER - TY - RPRT T1 - NCRN Meeting Spring 2015: Models for Multiscale Spatially-Referenced Count Data Y1 - 2015 A1 - Holan, Scott A1 - Bradley, Jonathan R. A1 - Wikle, Christopher K. AB - NCRN Meeting Spring 2015: Models for Multiscale Spatially-Referenced Count Data Holan, Scott; Bradley, Jonathan R.; Wikle, Christopher K. Presentation at the NCRN Meeting Spring 2015 PB - NCRN Coordinating Office UR - http://hdl.handle.net/1813/40176 ER - TY - JOUR T1 - Spatio-temporal change of support with application to American Community Survey multi-year period estimates JF - Stat Y1 - 2015 A1 - Bradley, Jonathan R. A1 - Wikle, Christopher K. A1 - Holan, Scott H. KW - Bayesian KW - change-of-support KW - dynamical KW - hierarchical models KW - mixed-effects model KW - Moran's I KW - multi-year period estimate AB - 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 US 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 hold-out set of 3-year period estimates. Copyright © 2015 John Wiley & Sons, Ltd. VL - 4 UR - http://dx.doi.org/10.1002/sta4.94 ER -