Donald Margaret R, Mengersen Kerrie L, Young Rick R
Mathematics and Statistics, University of New South Wales, Sydney, NSW, Australia.
Statistics Department, Queensland University of Technology, Brisbane, QLD, Australia.
PLoS One. 2015 Oct 29;10(10):e0141120. doi: 10.1371/journal.pone.0141120. eCollection 2015.
While a variety of statistical models now exist for the spatio-temporal analysis of two-dimensional (surface) data collected over time, there are few published examples of analogous models for the spatial analysis of data taken over four dimensions: latitude, longitude, height or depth, and time. When taking account of the autocorrelation of data within and between dimensions, the notion of closeness often differs for each of the dimensions. Here, we consider a number of approaches to the analysis of such a dataset, which arises from an agricultural experiment exploring the impact of different cropping systems on soil moisture. The proposed models vary in their representation of the spatial correlation in the data, the assumed temporal pattern and choice of conditional autoregressive (CAR) and other priors. In terms of the substantive question, we find that response cropping is generally more effective than long fallow cropping in reducing soil moisture at the depths considered (100 cm to 220 cm). Thus, if we wish to reduce the possibility of deep drainage and increased groundwater salinity, the recommended cropping system is response cropping.
虽然现在存在各种用于对随时间收集的二维（表面）数据进行时空分析的统计模型，但针对在纬度、经度、高度或深度以及时间这四个维度上采集的数据进行空间分析的类似模型，已发表的实例却很少。在考虑维度内部和维度之间数据的自相关性时，每个维度上的接近度概念通常有所不同。在此，我们考虑对这样一个数据集进行分析的多种方法，该数据集源自一项农业实验，旨在探究不同种植系统对土壤湿度的影响。所提出的模型在数据空间相关性的表示、假定的时间模式以及条件自回归（CAR）和其他先验的选择方面存在差异。就实质性问题而言，我们发现在所考虑的深度（100厘米至220厘米）下，响应式种植在降低土壤湿度方面通常比长期休耕种植更有效。因此，如果我们希望降低深层排水和地下水盐度增加的可能性，推荐的种植系统是响应式种植。
