Bayesian Spatial Modeling for Housing Data in South Africa.
Bayesian Spatial Modeling for Housing Data in South Africa.
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DOI：
10.1007/s13571-020-00233-y
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发表时间：
2021-11
影响因子：
0.8
通讯作者：
Gupta, Rangan
中科院分区：
文献类型：
--
作者：
Wang, Bingling;Banerjee, Sudipto;Gupta, Rangan
空间过程模型越来越多地用于分析地理编码位置上可用的数据。在本文中，我们构建了一个具有多元空间过程的分层框架，其中结果是“混合”的，有些可能是连续的，有些可能是二元的，有些可能是计数的。基本思想是通过分层构建条件分布来构建联合模型，每个条件分布中嵌入不同的空间过程。这个想法很简单，并且可以使用简单的贝叶斯计算方法（例如马尔可夫链蒙特卡罗方法）将所得模型拟合到多元空间数据。进行贝叶斯推理以进行参数估计和空间插值。所提出的模型使用在南非伊丽莎白港沃尔默区收集的住房数据进行说明。推理兴趣在于对相关结果的空间依赖性和独立解释变量的关联进行建模。不同模型的比较证实，通过结合空间过程，我们的数据集中的房屋售价相对更好地建模。
Spatial process models are being increasingly employed for analyzing data available at geocoded locations. In this article, we build a hierarchical framework with multivariate spatial processes, where the outcomes are “mixed” in the sense that some may be continuous, some binary and others may be counts. The underlying idea is to build a joint model by hierarchically building conditional distributions with different spatial processes embedded in each conditional distribution. The idea is simple and the resulting models can be fitted to multivariate spatial data using straightforward Bayesian computing methods such as Markov chain Monte Carlo methods. Bayesian inference is carried out for parameter estimation and spatial interpolation. The proposed models are illustrated using housing data collected in the Walmer district of Port Elizabeth, South Africa. Inferential interest resides in modeling spatial dependencies of dependent outcomes and associations accounting for independent explanatory variables. Comparisons across different models confirm that the selling price of a house in our data set is relatively better modeled by incorporating spatial processes.