  Recent developments in engineering techniques for spatial data collection
such as geographic information systems have resulted in an increasing need for
methods to analyze large spatial data sets. These sorts of data sets can be
found in various fields of the natural and social sciences. However, model
fitting and spatial prediction using these large spatial data sets are
impractically time-consuming, because of the necessary matrix inversions.
Various methods have been developed to deal with this problem, including a
reduced rank approach and a sparse matrix approximation. In this paper, we
propose a modification to an existing reduced rank approach to capture both the
large- and small-scale spatial variations effectively. We have used simulated
examples and an empirical data analysis to demonstrate that our proposed
approach consistently performs well when compared with other methods. In
particular, the performance of our new method does not depend on the dependence
properties of the spatial covariance functions.
