Bayesian Areal Interpolation, Estimation, and Smoothing: An Inferential Approach for Geographic Information Systems
Bayesian Areal Interpolation, Estimation, and Smoothing: An Inferential Approach for Geographic Information Systems
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贝叶斯面积插值、估计和平滑：地理信息系统的推理方法
DOI：
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发表时间：
1999
期刊：
影响因子：
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通讯作者：
Erin M. Conlon
中科院分区：
文献类型：
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作者：
Andrew S. Mugglin;B. Carlin;Lixing Zhu;Erin M. Conlon
地理信息系统为地理学家、林业工作者、统计学家、公共卫生官员和空间参照区域数据集的其他用户提供了强有力的工具。然而，尽管它们对于数据显示和趋势检测很有用，但它们通常具有很少的统计推断能力，使用户对所识别的各种模式和“热点”的重要性产生怀疑。不幸的是，经典的统计方法往往不适合这种复杂的推理任务，因为它处理的数据是多变量，多层次，不一致，往往非随机失踪。在本文中，我们描述了一个贝叶斯方法，同时允许缺失值的插值，估计相关协变量的影响，和空间平滑的潜在因果模式的推断问题。通过马尔可夫链蒙特卡罗（MCMC）计算方法实现，该方法自动产生点和区间估计，占数据中的所有不确定性来源。在一个简单的，理想化的例子的背景下描述的方法，我们说明了它与白血病发病率和潜在的地理风险因素在纽约汤普金斯县的数据集，总结了我们的结果与众多的地图创建使用流行的GIS弧/信息。
Geographic information systems (GISs) offer a powerful tool to geographers, foresters, statisticians, public health officials, and other users of spatially referenced regional data sets. However, as useful as they are for data display and trend detection, they typically feature little ability for statistical inference, leaving the user in doubt as to the significance of the various patterns and ‘hot spots’ identified. Unfortunately, classical statistical methods are often ill suited for this complex inferential task, dealing as it does with data which are multivariate, multilevel, misaligned, and often nonrandomly missing. In this paper we describe a Bayesian approach to this inference problem which simultaneously allows interpolation of missing values, estimation of the effect of relevant covariates, and spatial smoothing of underlying causal patterns. Implemented via Markov-chain Monte Carlo (MCMC) computational methods, the approach automatically produces both point and interval estimates which account for all sources of uncertainty in the data. After describing the approach in the context of a simple, idealized example, we illustrate it with a data set on leukemia rates and potential geographic risk factors in Tompkins County, New York, summarizing our results with numerous maps created by using the popular GIS Arc/INFO.