  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 transform by which multiple
integrals of covariance functions over irregular data regions may be
numerically approximated with the same level of accuracy to traditional
methods, but at a greatly reduced computational expense.
