摘要：Administrative data often arise as electronic copies of paid bills generated from insurance companies including the Medicare and Medicaid programs. Such data are widely seen and analyzed in the public health area, as in investigations of cancer control, health service accessibility, and spatial epidemiology. In areas like political science and education, administrative data are also important. Administrative data are sometimes more readily available as summaries over each administrative unit (county, zip code, etc.) in a particular set determined by geopolitical boundaries, or what statisticians refer to as areal data. However, the spatial dependence often present in administrative data is often ignored by health services researchers. This can lead to problems in estimating the true underlying spatial surface, including inefficient use of data and biased conclusions. In this article, we review hierarchical statistical modeling and boundary analysis (wombling) methods for areal-level spatial data that can be easily carried out using freely available statistical computing packages. We also propose a new edge-domain method designed to detect geographical boundaries corresponding to abrupt changes in the areal-level surface. We illustrate our methods using county-level breast cancer late detection data from the state of Minnesota.
关键词：Areal data;boundary analysis;hierarchical Bayesian model;Markov Chain Monte Carlo (MCMC) simulation;spatial statistics;wombling
