In the combined presence of the previous two assumptions—that is, when is constant and spatial increments define
—the SRF
is characterized as intrinsically stationary (Cressie, 1993, p. 40).
The expected value is the first statistical moment of the SRF
. The second statistical moment of the SRF
is the covariance
function between two points
and
in
, and it is defined as
When , the covariance expression provides the variance at
.
The assumption of a constant means that the expected value is invariant with respect to translations of the spatial location
. The covariance is considered invariant to such translations when it depends only on the distance
between any two points
and
. If both of these conditions are true, then the preceding expression becomes
When both and
are invariant to spatial translations, the SRF
is characterized as second-order stationary (Cressie, 1993, p. 53).
In a second-order stationary SRF the quantity is the same for any two points that are separated by distance
. Based on the preceding formula, for
you can see that the variance is constant throughout a second-order stationary SRF. Hence, second-order stationarity is a
stricter condition than intrinsic stationarity.
Under the assumption of second-order stationarity, the semivariance definition at the beginning of this section leads to the conclusion that
which relates the theoretical semivariance and covariance. Keep in mind that the empirical estimates of these quantities are not related in exactly the same way, as indicated in Schabenberger and Gotway (2005, section 4.2.1).