Nonparametric Hierarchical Modeling for Detecting Boundaries in Areally Referenced Spatial Datasets
Nonparametric Hierarchical Modeling for Detecting Boundaries in Areally Referenced Spatial Datasets
复制标题
用于检测区域参考空间数据集中边界的非参数层次建模
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
A. McBean
中科院分区：
文献类型：
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作者：
Pei Li;Sudipto Banerjee;T. Hanson;A. McBean
随着地理信息系统 (GIS) 软件的可及性不断提高，公共卫生领域的研究人员和管理人员越来越多地遇到空间参考数据集。空间数据分析的推理兴趣通常不在于统计估计的地图本身，而在于地图上“边缘”或“边界”的正式识别。边界可以被认为是一组相互连接的空间位置，它们分隔具有不同特征的区域。本文提出了一类非参数贝叶斯模型，以解释各个层面的不确定性，以得出快速变化的空间区域，从而表明驱动这些差异的隐藏风险因素。进行模拟研究来说明新方法并与现有方法进行比较。使用所提出的方法检测明尼苏达州 SEER-Medicare 计划的肺炎和流感住院地图上的“边界”。
With increasing accessibility to Geographical Information Systems (GIS) software, researchers and administrators in public health are increasingly encountering spatially referenced datasets. Inferential interest of spatial data analysis often resides not in the statistically estimated maps themselves, but on the formal identification of “edges” or “boundaries” on the map. Boundaries can be thought of as a set of connected spatial locations that separate areas with different characteristics. A class of nonparametric bayesian models are proposed in this paper to account for uncertainty at various levels to elicit spatial zones of rapid change that suggest hidden risk factors driving these disparities. Simulation study are conducted to illustrate the new approaches and compare with existing methods. “Boundaries” on Pneumonia and Influenza hospitalization map from the SEER-Medicare program in Minnesota are detected using the proposed approaches.