Spatial soil applications frequently involve binomial variables. If relevant environmental covariates are available, using a Bayesian generalized linear model (BGLM) might be a solution for mapping such discrete soil properties. The geostatistical extension, a Bayesian generalized linear geostatistical model (BGLGM), adds spatial dependence and is thus potentially better equipped. The objective of this work was to evaluate whether it pays off to extend from a BGLM to a BGLGM for mapping binary soil properties, evaluated in terms of prediction accuracy and modelling complexity. As motivating example, we mapped the presence/absence of the Pleistocene sand layer within 120 cm from the land surface in the Dutch province of Flevoland, using the BGLGM implementation in the R-package geoRglm. We found that BGLGM yields considerably better statistical validation metrics compared to a BGLM, especially with – as in our case – a large (n = 1,000) observation sample and few relevant covariates available. Also, the inferred posterior BGLGM parameters enable the quantification of spatial relationships. However, calibrating and applying a BGLGM is quite demanding with respect to the minimal required sample size, tuning the algorithm, and computational costs. We replaced manual tuning by an automated tuning algorithm (which eases implementing applications) and found a sample composition that delivers meaningful results within 50 h calculation time. With the gained insights and shared scripts spatial soil practitioners and researchers can – for their specific cases – evaluate if using BGLGM is feasible and if the extra gain is worth the extra effort.

中文翻译：

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使用贝叶斯广义线性地质统计模型将深度映射到更新世沙子

空间土壤应用经常涉及二项式变量。如果相关环境协变量可用，则使用贝叶斯广义线性模型 (BGLM) 可能是绘制此类离散土壤特性的解决方案。地统计扩展，贝叶斯广义线性地统计模型 (BGLGM)，增加了空间依赖性，因此可能更好地配备。这项工作的目的是评估从 BGLM 扩展到 BGLGM 以绘制二元土壤特性是否值得，根据预测准确性和建模复杂性进行评估。作为激励示例，我们使用 R 包 geoRglm 中的 BGLGM 实现，绘制了距荷兰弗莱沃兰省地表 120 厘米范围内更新世沙层的存在/不存在。n  = 1,000) 观察样本和可用的相关协变量很少。此外，推断的后 BGLGM 参数可以量化空间关系。然而，校准和应用 BGLGM 在所需的最小样本量、调整算法和计算成本方面要求很高。我们用自动调整算法（简化了应用程序的实施）取代了手动调整，并找到了一个样本组合，可以在 50 小时的计算时间内提供有意义的结果。借助获得的见解和共享脚本，空间土壤从业者和研究人员可以针对他们的具体案例评估使用 BGLGM 是否可行，以及额外的收益是否值得付出额外的努力。