In many practical applications, spatial data are often collected at areal levels (i.e., block data), and the inferences and predictions about the variable at points or blocks different from those at which it has been observed typically depend on integrals of the underlying continuous spatial process. In this paper, we describe a method based on Fourier transforms by which multiple integrals of covariance functions over irregular data regions may be numerically approximated with the same level of accuracy as traditional methods, but at a greatly reduced computational expense.

中文翻译：

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不规则数据区域上集成协方差函数的快速数值逼近方法

在许多实际应用中，空间数据通常是在区域级别上收集的（即块数据），有关变量的推论和预测在不同于已观察到的点或块的情况下，通常取决于底层连续空间的积分处理。在本文中，我们描述了一种基于傅立叶变换的方法，通过该方法，可以以与传统方法相同的精度级别对不规则数据区域上的协方差函数的多个积分进行数值逼近，但是却大大降低了计算费用。