Fully Model-Based Approaches for Spatially Misaligned Data
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空间边界不对齐数据的完全模型化方法
- 研究问题
- 当多个变量按不同区域边界汇总时，如何超越地图叠加的描述分析，进行信息整合、空间重新分配及统计推断？
- 方法与思路
- 作者建立层级贝叶斯模型，以条件独立的泊松—多项分配处理非嵌套区域，用条件自回归先验描述空间相关并进行计算。
- 材料陈述的贡献
- 摘要称，该框架能整合多源数据、先验、协变量及多层响应，并处理边缘区域缺失；应用展示人口计数向厂区风向网格重分配。
- 阅读关注点
- 应核对跨区域计数分配与辅助建筑计数的作用、空间先验及缺失机制，并检查 Metropolis–Hastings 计算和不确定性传播。
- 阅读边界
- 仅据版本未注明的索引摘要；重分配依赖模型与先验，摘要未说明校验结果，不能将人口空间重分配视为暴露的因果效应估计。
原文摘要
We consider inference using multivariate data that are spatially misaligned; that is, involving variables (typically counts or rates) that are aggregated over differing sets of regional boundaries. Geographic information systems enable the simultaneous display of such datasets, but their current capabilities are essentially only descriptive, not inferential. We describe a hierarchical modeling approach that provides a natural solution to this problem through its ability to sensibly combine information from several sources of data and available prior information. Illustrating in the context of counts, allocation under nonnested regional grids is handled using conditionally independent Poisson-mullinomial models. Explanatory covariales and multilevel responses are also easily accommodated, with spatial correlation modeled using a conditionally autoregressive prior structure. Methods for dealing with missing values in spatial “edge zones” are also discussed. Like many recent hierarchical Bayesian applications, computing is implemented via a carefully tailored Metropolis-Hastings algorithm. We illustrate our method with a complex dataset involving inhalation exposure to radon emanating from a depleted uranium fuel processing plant in southwestern Ohio. Structure counts (obtained from U.S. Geological Survey topographical maps) are used to realign sex- and age group-specific U.S. census block group population counts onto a 160-cell circular “windrose” centered at the plant.