The Impact of spatial scales and spatial smoothing on the outcome of Bayesian Spatial Model: summary of results for fitting four models at various spatial scales for various spatial patterns

An investigation was completed to assess the impact of changes in
spatial scale on model outcome for a set of spatial structures and to
evaluate the performance of various Bayesian spatial smoothness priors
for
spatial dependence, namely an intrinsic Gaussian Markov random field
(IGMRF), a second-order random walk (RW2D) on a lattice, and a Gaussian
field with Matérn correlation function.The dataset presents a summary of results for fitting four models at various spatial scales for various patterns.

本研究已完成，旨在评估空间尺度变化对一组空间结构的模型结果的影响，并评估多种贝叶斯空间平滑先验在空间相关性建模中的表现，具体包括内在高斯马尔可夫随机场（intrinsic Gaussian Markov random field，IGMRF）、格点上的二阶随机游走（second-order random walk，RW2D）以及带有Matérn相关函数的高斯场。本数据集汇总了针对多种空间模式，在不同空间尺度下拟合四类模型的结果。