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5 Semivariogram (SV) Regional Dependence Measure

5 Semivariogram (SV) Regional Dependence Measure

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5.5 Semivariogram (SV) Regional Dependence Measure

Fig. 5.7 Homogeneous,

isotropic, and uniform ReV






Fig. 5.8 Perfectly

homogeneous and isotropic


γ (d)



The simplest and most common form of ReV is a triplet, and therefore it is

illuminating first to consider the surface in 3D, and then according to the SV

definition, it is possible to infer its shape intuitively by mental experiment:

1. Continuously deterministic uniform spatial data: If the ReV is a deterministic

horizontal surface of homogeneous, isotropic, and uniform data as in Fig. 5.7,

then the average half-square difference of such data is zero at every distance as

in Fig. 5.8.

2. Discontinuously deterministic partially uniform spatial data: The continuity in

Fig. 5.7 is disrupted by a discontinuous feature (cliff, fault, facies change,

boundary, etc.) as in Fig. 5.9.

The average square difference at various distances leads to an SV with a

discontinuity at the origin (see Fig. 5.10), the amount of which is equal to the

square difference between higher, ZH(x, y), and lower, ZL(x, y), data values as

d ị ẳ ẵZH x, yÞ À Z L ðx, yފ2


The resulting SV is expected to take the shape as in Fig. 5.10, where there is a

nonzero value at the origin. Such a jump at the origin indicates discontinuity

embeddings in the spatial event and it is referred to as “sill” in geostatistical



5 Spatial Dependence Measures

Z(x, y)

ZH(x, y)

ZL(x, y)

[ZH(x, y) – ZL(x, y)]




Fig. 5.9 Discontinuous surface

Fig. 5.10 Completely

random ReV SV

γ (d)

[ZH(x, y) – ZL(x, y)]2



3. Continuously deterministic spatially linear trend data: If the ReV is a linear

surface along the x axis as in Fig. 5.11, then the SV along the x axis by definition

has a quadratic form without any decrease (Fig. 5.12).

This SV does not have any horizontal portion, and at large distances, the slope

increases in an extreme manner.

4. Discontinuously deterministic spatially linear trend data: If the trend surface in

Fig. 5.11 has a discontinuity (Fig. 5.13), then the SV shape appears as in

Fig. 5.14, where there is a jump at the origin, which is referred to as nugget

effect in SV terminology.

5. Completely independent spatial data: If the ReV is completely random with no

spatial correlation as in Fig. 5.15, then the SV will be equal to the variance, σ 2, of

the ReV at all distances as in Fig. 5.16. A decision can be made about the

continuity (or discontinuity) and smoothness of the ReV by visual inspection

from the sample SV. If at small distances the sample SV does not indicate

passage from the origin (nugget effect), then the ReV includes discontinuities,

where there is no regional dependence in the ReV at all. Its SV appears as a

horizontal straight line similar to SV in Fig. 5.10.

5.5 Semivariogram (SV) Regional Dependence Measure


Fig. 5.11 Continuous

linear trend





Fig. 5.12 Linear trend

surface SV in x direction

γ (d)






Fig. 5.13 Discontinuous trend surface



5 Spatial Dependence Measures

Fig. 5.14 Discontinuous

trend surface SV in x


γ (d)




Fig. 5.15 Independent spatial data

Fig. 5.16 Completely

random ReV SV

γ (d)




The SV in this spatial random event case is equivalent to the expectation of

Eq. 5.2, which after expansion and expectation E(.) operation application on both

sides leads to





E½γ dị ẳ E Z2H x, yị 2EẵZ H x, yịZL x, yị ỵ E Z2L x, yị

Since the ReV is assumed as spatially independent with zero mean (expectation),

the second term of this expression is equal to zero and the other terms are equal to

the variance, σ 2, of the spatial event. Finally, this last expression yields

Eẵ d ị ẳ 2σ 2 . In order to have the SV expectation equal to the variance in practical

applications, it is defined as the half-square difference instead of square difference

5.5 Semivariogram (SV) Regional Dependence Measure


as in Eq. 5.2. Consequently, the SV of an independent ReV appears as having a sill

value similar to Fig. 5.10 but this time the sill value is equal to the spatial variance

of the ReV.


SV Definition

The SV is the basic geostatistical tool for visualizing, interpreting, modeling, and

exploiting the regional dependence in a ReV. It is well known that even though the

measurement sites are irregularly distributed, one can find central statistical parameters such as mean, median, mode, variance, skewness, etc., but they do not yield

any detailed information about the phenomenon concerned. The greater the variance the greater is the variability, but unfortunately this is a global interpretation

without detailed useful information. The structural variability in any phenomenon

within an area can best be measured by comparing the relative change between two

sites. For instance, if any two sites, distant d apart, have measured concentration

values Zi and Zi+d, then the relative variability can simply be written as (Zi ÀZi+d).

However, similar to Taylor (1915) theory concerning turbulence, the square difference, Z i Z iỵd ị2 , represents this relative change in the best possible way. This

square difference has appeared first in the Russian literature as the “structure

function” of ReV. It subsumes the assumption that the smaller the distance, d, the

smaller will be the structure function. Large variability implies that the degree of

dependence among earth sciences records might be rather small even for sites close

to each other.

In order to quantify the degree of spatial variability, variance and correlation

techniques have been frequently used in the literature. However, these methods

cannot account correctly for the spatial dependence due to either non-normal PDFs

and/or irregularity of sampling positions.

The classical SV technique has been proposed by Matheron (1965) to eliminate

the aforementioned drawbacks. Mathematically, it is defined as a version of

Eq. 5.26 by considering all of the available sites within the study area as (Matheron

1965; Clark 1979)

d ị ẳ


1 X

Zi Ziỵd Þ2

2nd k¼1


where k is the counter of the distance which can be expanded by considering the

regional arithmetic average, Z, of the ReV as follows:




Zi Z Ziỵd À Z

2 k¼1





Á2 i

¼ Z i À Z À 2 Zi Z Ziỵd Z ỵ Ziỵd Z

d ị ẳ


5 Spatial Dependence Measures

The elegancy of this formulation is that the ReV PDF is not important in obtaining

the SV, and furthermore, it is effective for regular data points. It is to be recalled,

herein, that the classical variogram, autocorrelation, and autorun techniques (S¸en

1978) all require equally spaced data values. Due to the irregularly spaced point

sources, the use of classical techniques is highly questionable, except that these

techniques might provide biased approximate results only. The SV technique,

although suitable for irregularly spaced data, has practical difficulties as summarized by Sen (1989). Among such difficulties is the grouping of distance data into

classes of equal or variable lengths for SV construction, but the result appears in an

inconsistent pattern and does not have a nondecreasing form as expected in theory.

As the name implies a SV, γ(d ), is a measure of spatial dependence of a ReV.

Due to independence any cross multiplication of Zi and Zj will be equal to zero

on the average, and hence this is equivalent to regional variance, σ 2, as explained in

the previous section. Figure 5.16 shows this mental experiment SV as a horizontal

straight line. Hence, at every distance, the SV is dominated by sill value only.

Expert reasoning of SV models in the previous figures helps to elaborate some

fundamental and further points as follows:

1. If the ReV is continuous without any discontinuity, then the SV should start from

the origin, which means that at zero distance, SV is also zero (Figs. 5.8 and 5.12).

2. If there is any discontinuity within the ReV, then at zero distance, a nonzero

value of the SV appears as in Figs. 5.10, 5.14, and 5.16.

3. If there is an extensive spatial dependence, then the SV has increasing values at

large distances (Figs. 5.12 and 5.14).

4. When the spatial dependence is not existent, then the SV has a constant nonzero

value equal to the regional variance of the ReV at all distances as in Fig. 5.16.

5. Under the light of all what have been explained so far, it is logically and

rationally obvious that in the case of spatial dependence structure in ReV, the

SV should start from zero at zero distance and then will reach the regional

variance value as a constant at large distances. The SV increases as the distance

increases until at a certain distance away from a point, it equals the variance

around the average value of the ReV and will therefore no longer increase,

causing a flat (stabilization) region to occur on the SV, which is called as a sill

(Fig. 5.17). The horizontal stabilization level of sample SV is referred to as its

sill. The distance at which the horizontal SV portion starts is named as the

range, R, radius of influence or dependence length after which there is no spatial

(regional) dependence between data points. Only within this range, locations are

related to each other, and hence all measurement locations in this region are the

nearest neighbors that must be considered in the estimation process. This implies

that the ReV has a limited areal extend over which the spatial dependence

decreases or independence increases in the SV sense as in Fig. 5.17.

The classical SV is used to quantify and model spatial correlations. It reflects

the idea that closer points have more regional dependence than distant points. In

general, spatial prediction is a methodology that embeds the spatial dependence

in the model structure,

5.5 Semivariogram (SV) Regional Dependence Measure

Fig. 5.17 Classical global

SV and elements

γ (d)


Range (radius of influence)


Sill (regional variance)

Nugget effect





γ (d)

γ (d)





Fig. 5.18 Classical directional SV, (a) major axis, (b) minor axis

6. At some distance, called the range, the SV will become approximately equal to

the variance of the ReV itself (see Fig. 5.17). This is the greatest distance over

which the value at a point on the surface is related to the value at another point.

The range defines the maximum neighborhood over which control points should

be selected to estimate a grid node, to take advantage of the statistical correlation

among the observations. In the circumstance where the grid node and the

observations are spaced so that all distances exceed the range, Kriging produces

the same estimate as classical statistics, which is equal to the mean value.

7. However, most often natural data may have preferred orientations, and as a

result, ReV values may change more along the same distance in one direction

than another (Fig. 5.3). Hence, in addition to distance, the SV becomes a

function of direction (Fig. 5.18).

It is possible to view the general of SV as a 3D function as the change of SV

value, γ(θ,d ), with respect to direction, θ, and separation distance, d. Of course, θ

and d are the independent variables. In general, specification of any SV requires

the following information:





Sill (regional variance)

Range (radius of influence)

Nugget (zero distance jump)

Directional values of these parameters


5 Spatial Dependence Measures

The last point is helpful for the identification of regional isotropy or anisotropy.

For the Kriging application, the convenient composition of these parameters must

be identified through a theoretical SV. Whether a given sample SV is stationary or

not can be decided from its behavior at large distances. If the large distance portion

of the SV approaches a horizontal line, then it is stationary, which means intuitively

that there are rather small fluctuations with almost the same variance at every corner

of the region.

If the SV is generated from paired points selected just based on distance (with no

directional component), then it is called isotropic (iso means the same; tropic refers

to direction) or omnidirectional. In this case, the lag-distance measure is a scalar

and the SV represents the average of all pairs of data without regard to their

orientation or direction. A standardized SV is created by dividing each SV value

by the overall sample variance, which allows SVs from different data sets on the

same entity for facilitating the mutual comparison.

On the other hand, SVs from points that are paired based on direction and

distance are called anisotropic (meaning not isotropic). In this case, the lag measure

is a vector. The SVs in this case are calculated for data that are in a particular

direction as explained in Sect. 4.3. The regularity and continuity of the ReV of a

natural phenomenon are represented by the behavior of SV near the origin. In SV

models with sill (Fig. 5.17), the horizontal distance between the origin and the end

of SV reflects the zone where the spatial dependence and the influence of one value

on the other occur, and beyond this distance, the ReV Z(x) and Z(x + d) are

independent from each other. Furthermore, SVs, which increase at least as rapidly

as d2 for large distances d, indicate the presence of drift (trend), i.e., nonstationary

mathematical expectation. Plot of SV graphs for different directions gives valuable

information about continuity and homogeneity. If SV depends on distance d only, it

is said to be isotropic, but if it depends on distance as well as direction, it is said to

be anisotropic. A properly fitted theoretical SV model allows linear estimation

calculations that reflect the spatial extent and orientation of spatial dependence in

the ReV to be mapped. Details on these points can be found in standard textbooks

on geostatistics (Davis 1986; Clark 1979).

There are also indicator SVs which are calculated from data that have been

transformed to a binary form (1 or 0), indicating the presence or absence of some

variable or values that are above some threshold. In the calculation of sample SVs,

the following rules of thumb must be considered:

1. Each distance lag (d) class must be represented by at least 30–50 pairs of points.

2. The SV should only be plotted out to about half the width of the sampling space

in any direction.

Characterizing spatial correlation across the site through experimental SV can

often be the most time-consuming step in a geostatistical analysis. This is particularly true if the data are heterogeneous or limited in number. Without a rationale

for identifying the major direction of anisotropy, the following steps might be

useful in narrowing the focus of the exercise:

5.5 Semivariogram (SV) Regional Dependence Measure


1. Begin with an omnidirectional SV with a bandwidth large enough to encompass

all data points on the site. In practice, maximum lag distance can be taken as one

third of the maximum distance between the data points.

2. Select the number of lags and lag distances sufficient to span a significant portion

of the entire site, and choose the lag tolerance to be very close in value to the lag

distance itself.

3. Calculate the SV. In most cases, data become less correlated as the distance

between them increases. Under these circumstances, the SV values should

produce a monotonic increasing function, which approaches a maximal value

called the sill. In practice, this may not be the case with SV values that may

begin high or jump around as distance increases.

4. Adjust the number of lags and lag tolerances until, generally, a monotonic

increasing trend is seen in the SV values. If this cannot be achieved, it may be

that a geostatistical approach is not viable or that more complicated trends are

occurring than can be modeled. If a visual inspection of the data or knowledge

about the dispersion of contamination indicates a direction of correlation, it may

be more appropriate to first test this direction.

5. Assuming the omnidirectional SV is reasonable, add another direction to the plot

with a smaller tolerance. You may have to adjust the bandwidth and angle

tolerance to produce a reasonable SV plot.

6. If the second direction rises slower to the sill or rises to a lower sill, then this is

the major direction of anisotropy.

7. If neither direction produces significantly lower spatial correlation, it may be

reasonable to assume an isotropic correlation structure.

8. Add a cone structure with direction equal to the major direction plus 90 , and

model the SV results in this direction.

9. If the data are isotropic, choose the omnidirectional SV as the major direction.


SV Limitations

The SV model mathematically specifies the spatial variability of the data set, and

after its identification, the spatial interpolation weights, which are applied to data

points during the grid node calculations, are direct functions of the Kriging model

(Chap. 5). In order to determine the estimation value, all measurements within the

SV range are assigned weights depending on the distance of neighboring point

using the SV. These weights and measurements are then used to calculate the

estimation value through Kriging modeling. Useful and definite discussions on

the practicalities and limitations of the classicaltheoretical function, which is called

SV have been given by Sen (1989) as follows:

1. The classical SV, γ(d ), for any distance, d, is defined as the half-square difference of two measurements separated by this distance. As d varies from zero to

the maximum possible distance within the study area, the relationship of the

half-square difference to the separation distance emerges as a theoretical


5 Spatial Dependence Measures

function, which is called the SV. The sample SV is an estimate of this theoretical

function calculated from a finite number, n, of samples. The sample SV can be

estimated reliably for small distances when the distribution of sampling points

within the region is regular. As the distance increases, the number of data pairs

for calculation of SV decreases, which implies less reliable estimation at large


2. In various disciplines of the earth sciences, the sampling positions are irregularly

distributed in the region, and therefore, an unbiased estimate of SV is not

possible. Some distances occur more frequently than others and accordingly

their SV estimates are more reliable than others. Hence, a heterogeneous reliability dominates the sample SV. Consequently, the sample SV may have ups

and downs even at small distances. Such a situation gives rise to inconsistencies

and/or experimental fluctuations with the classical SV models which are, by

definition, nondecreasing functions, i.e., a continuous increase with distance is

their main property. In order to give a consistent form to the sample SV, different

researchers have used different subjective procedures:

(a) Journel and Huijbregts (1978) advised grouping of data into distance classes

of equal length in order to construct a sample SV. However, the grouping of

data pairs into classes causes a smoothing of the sample SV relative to the

underlying theoretical SV. If a number of distances fall within a certain

class, then the average of half-square differences within this class is taken as

the representative half-square difference for the mid-class point. The effect

of outliers is partially damped, but not completely smoothed out by the

averaging operation.

(b) To reduce the variability in the sample SV, Myers et al. (1982) grouped the

observed distances between samples into variable length classes. The class

size is determined such that a constant number of sample pairs fall in each

class. The mean values of distances and half-square differences are used for

the classes as a representative point of sample SV. Even this procedure

resulted in an inconsistent pattern of sample SV (Myers et al. 1982) for

some choices of the number, m, of pairs falling within each class. However,

it was observed by Myers et al. that choosing m ¼ 1000 gave a discernible

shape. The choice of constant number of pairs is subjective, and in addition,

the averaging procedures smooth out the variability within the experimental

SV. As a result the sample SV provides a distorted view of the variable in

that it does not provide, for instance, higher-frequency (short wavelength)

variations. However, such short wavelength variations, if they exist, are so

small that they can be safely ignored.

The above procedures have two basic common properties, namely, predetermination of a constant number of pairs or distinctive class lengths and the arithmetic

averaging procedure for half-square differences as well as the distances. The former

needs a decision, which in most cases is subjective, whereas the latter can lead to

unrepresentative SV values. In classical statistics, only in the case of symmetrically

distributed data, the mean value is the best estimation; otherwise, the median

5.6 Sample SV


becomes superior. Moreover, the mean value is sensitive to outliers. The following

points are important in the interpretation of any sample SV:

1. The SV has the lowest value at the smallest lag distances (d ) and increases with

distance, leveling off at the sill, which is equivalent to the overall regional

variance of the available sample data. It is the total vertical scale of the SV

(nugget effect + sum of all component scales). However, linear, logarithmic, and

power SVs do not have a sill.

2. The range is the average distance (lag) within which the samples remain

spatially dependent and it corresponds to the distance at which the SV values

level off. Some SV models do not have a length parameter; e.g., the linear model

has a slope instead,

3. The nugget is the SV value at which the model appears to intercept the ordinate.

It quantifies the sampling and assaying errors and the short-scale variability (i.e.,

spatial variation that occurs at distance closer than the sample spacing). It

represents two often co-occurring sources of variability:

(a) All unaccounted for spatial variability at distances smaller than the smallest

sampling distance.

(b) Experimental error is often referred to as human nugget. According to

Liebhold et al. (1993), interpretations made from SVs depend on the size

of the nugget because the difference between the nugget and the sill (if there

is one) represents the proportion of the total sample variance that can be

modeled as spatial variability.


Sample SV

In practice, one is unlikely to get SVs that look like the one shown in Fig. 5.17.

Instead, patterns such as those in Fig. 5.19 are more common.

Important practical information in the interpretation and application of any

sample SV is to consider only about d/3 of the horizontal distance axis values

from the origin as reliable.

A digression is taken in this book as for the calculation of sample SVs. Instead of

easting- and northing-based SVs, it is also possible to construct SVs based on triple

variables. In the following, different triple values are assessed for the SV shapes and

interpretations. For instance, in Fig. 5.20, the chloride change with respect to

calcium and sodium is shown in 3D and various sample SVs along different

directions are presented in Fig. 5.21.

This figure indicates that the change of chloride data with respective independent variables (magnesium and calcium) is of clumped type without leveling effect.

It is possible to consider Fig. 5.20 as having two parts, namely, an almost linear

trend and fluctuations (drift) around it. In such a case, a neighborhood definition and

weight assignments become impossible. Therefore, the ReV is divided into two

parts, the residual and the drift. The drift is the weighted average of points within

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