  There are various methods to analyze different kinds of data sets. Spatial
data is defined when data is dependent on each other based on their respective
locations. Spline and Kriging are two methods for interpolating and predicting
spatial data. Under certain conditions, these methods are equivalent, but in
practice they show different behaviors. Amount of data can be observed only at
some positions that are chosen as positions of sample points, therefore,
prediction of data values in other positions is important. In this paper, the
link between Spline and Kriging methods is described, then for an
epidemiological two dimensional real data set, data is observed in geological
longitude and in latitude dimensions, and behavior of these methods are
investigated. Comparison of these performances show that for this data set,
Kriging method has a better performance than Spline method.
