Scott Holan, Principal Investigator
Noel Cressie, Co-Principal Investigator
Christopher Wikle, Co-Principal Investigator
The University of Missouri is focusing on the American Community Survey. The American Community Survey (ACS) is an ongoing survey that releases data annually, providing communities with the timely information needed to plan the distribution of resources and services. According to the U.S. Census Bureau, the data from the survey provides input into how more than $400 billion in federal and state funds are distributed annually. Census released the first five-year period estimates associated with the ACS for all standard tabulation areas in 2010. This marks an interesting time in U.S. history as Census shifts from the decennial census long-form data to using an ongoing survey that releases data annually. Making this transition presents many methodological challenges, both for Census and for data users. The hierarchical multiscale spatio-temporal statistical models and expansive statistical methodology in this project will address many of these challenges and facilitate broader and more effective utilization of the ACS. In particular, this project will develop an efficient framework for carrying out small area estimation while preserving geographical and temporal constraints that arise from the aggregate structure found in the ACS. Further, by borrowing strength across multiple scales in space and time and multiple outcomes, the approach will reduce the variance in the ACS small area estimates and its derivatives. Additionally, from a data-user perspective, the methodology will simultaneously provide coherent estimates on several temporal scales rather than being hampered by the published multiyear estimates, allowing researchers to compare trends across different geographic scales and units.
This project will investigate methodology that provides novel solutions across a wide-range of applied problems. The research will improve the interpretability and usability of the ACS through the development of hierarchical multiscale spatio-temporal statistical models. In addition, the project will provide a variety of methods that are of independent interest and can be used in many other surveys administered by Census and other federal statistics agencies. Several of the proposed methods also will directly carry over to the area of disease mapping and thus provide important tools for public health. The project will contribute to the statistics literature and will be of value to the work of government agencies and many subject-matter disciplines. One of the major focuses of this project will be to educate and train graduate students and postdoctoral researchers. It is expected that students will acquire the requisite skills for possible integration into the federal sector.