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ⓘ Variogram



Variogram
                                     

ⓘ Variogram

Variogram sacara téoritis 2 γ x, y {\displaystyle 2\gamma x,y} nyaéta hiji fungsi nu ngajéntrékeun tingkat pakaitna spasial tina random field atawa stochastic process Z x {\displaystyle Zx} spasial. It is defined as the expected squared increment of the values between locations x {\displaystyle x} and y {\displaystyle y} Wackernagel 2003:

2 γ x, y = E | Z x − Z y | 2) {\displaystyle 2\gamma x,y=E\left|Zx-Zy|^{2}\right)}

γ x, y {\displaystyle \gamma x,y} itself is called semivariogram. In case of a stationary process the variogram and semivariogram can be represented as a function γ s h = γ 0, 0 + h {\displaystyle \gamma _{s}h=\gamma 0.0+h} of the difference h = y − x {\displaystyle h=y-x} between locations only, by the following relation Cressie 1993:

γ x, y = γ s y − x {\displaystyle \gamma x,y=\gamma _{s}y-x}

If the process is furthermore isotropic, then variogram and semivariogram can be represented by a function γ i h:= γ s h e 1 {\displaystyle \gamma _{i}h:=\gamma _{s}he_{1}} of the distance h = ‖ y − x ‖ {\displaystyle h=\|y-x\|} only Cressie 1993:

γ x, y = γ i h {\displaystyle \gamma x,y=\gamma _{i}h}

The indexes i {\displaystyle i} or s {\displaystyle s} are typically not written. The terms are used for all three forms of the function. Moréover the term variogram is sometimes used for semivariogram and the symbol γ {\displaystyle \gamma } for the variogram, which brings some confusion.

                                     

1. Sifat-sifatna

According to the théoretical variogram has the following properties:

  • A function is a semivariogram if and only if it is a conditionally negative definite function, i.e. for all weights w 1, …, w N {\displaystyle w_{1},\ldots,w_{N}} subject to ∑ i = 1 N w i = 0 {\displaystyle \sum _{i=1}^{N}w_{i}=0} and locations x 1, …, x N {\displaystyle x_{1},\ldots,x_{N}} it holds
  • The semivariogram is nonnegative γ x, y ≥ 0 {\displaystyle \gamma x,y\geq 0}, since it is the expectation of a square.
  • The semivariogram γ x, x = γ i 0 = E Z x − Z x) 2) = 0 {\displaystyle \gamma x,x=\gamma _{i}0=E\leftZx-Zx)^{2}\right)=0} at distance 0 is always 0, since Z x − Z x = 0 {\displaystyle Zx-Zx=0}.

∑ i = 1 N ∑ j = 1 N w i γ x i, x j w j ≤ 0 {\displaystyle \sum _{i=1}^{N}\sum _{j=1}^{N}w_{i}\gamma x_{i},x_{j}w_{j}\leq 0}

which corresponds to the fact that the variance v a r X {\displaystyle varX} of X = ∑ i = 1 N w i Z x i {\displaystyle X=\sum _{i=1}^{N}w_{i}Zx_{i}} is given by the negative of this double sum and must be nonnegative.

  • If the covariance function of a process exists it is related to variogram by
  • As a consequence the semivariogram might be non continuous only at the origin. The height of the jump at the origin is sometimes referred to as nugget or nugget effect.

2 γ x, y = C x, x + C y, y − 2 C x, y {\displaystyle 2\gamma x,y=Cx,x+Cy,y-2Cx,y}

  • If the random field is stationary and ergodic, the lim h → ∞ γ s h = v a r Z x) {\displaystyle \lim _{h\to \infty }\gamma _{s}h=varZx)} corresponds to the variance of the field. The limit of the semivariogram is also called its sill.
  • γ x, y = E | Z x − Z y | 2) = γ y, x {\displaystyle \gamma x,y=E|Zx-Zy|^{2})=\gamma y,x} is a symmetric function.
  • Consequently γ s h = γ s − h {\displaystyle \gamma _{s}h=\gamma _{s}-h} is an even function.
  • If a stationary random field has no spatial dependence i.e. C h = 0 {\displaystyle Ch=0} if h ≠ 0 {\displaystyle h\not =0}) the semivariogram is the constant v a r Z x) {\displaystyle varZx)} everywhere except at the origin, where it is zero.
                                     

2. Variogram Empiris

For observations z i, i = 1, …, N {\displaystyle z_{i},\;i=1,\ldots,N} at locations x 1, …, x N {\displaystyle x_{1},\ldots,x_{N}} the empirical variogram γ ^ h {\displaystyle {\hat {\gamma }}h} is defined as Cressie 1993:

γ ^ h:= 1 | N h | ∑ i, j ∈ N h | z i − z j | 2 {\displaystyle {\hat {\gamma }}h:={\frac {1}{|Nh|}}\sum _{i,j\in Nh}|z_{i}-z_{j}|^{2}}

where N h {\displaystyle Nh} denotes the set of pairs of observation i, j {\displaystyle i,\;j} placed at an approximate distance of h {\displaystyle h}. Here "approximate distance h {\displaystyle h} is not exactly defined and typically implemented by a certain tolerance.

The empirical variogram is used in géostatistik as a first estimate of the théoretical variogram needed for spatial interpolation by kriging.

According Cressie 1993 for observations z i = Z x i {\displaystyle z_{i}=Zx_{i}} from a stationary random field Z x {\displaystyle Zx} the empirical variogram with lag tolerance 0 is an unbiased éstimator of the théoretical variogram, due to

E ={\frac {1}{2|Nh|}}\sum _{i,j\in Nh}2\gamma x_{j}-x_{i}={\frac {2|Nh|}{2|Nh|}}\gamma h}

See the controversy discussion on the correctness of the scaling factor below.

                                     

3. Parameter Variogram

The following paraméters are often used to describe variograms:

  • sill s {\displaystyle s}: Limit of the variogram tending to infinity lag distances.
  • nugget n {\displaystyle n}: The height of the jump of the semivariogram at the discontinuity at the origin.
  • range r {\displaystyle r}: The distance in which the difference of the variogram from the sill gets neglectable. The exact position, where the difference gets "neglectable" is quite imprecise.
                                     

4. Model Variogram

The empirical variogram cannot be computed at every lag distance h {\displaystyle h} and due to variation in the estimation it is not ensured that it is a valid variogram, as defined above. However some Geostatistical methods such as kriging need valid semivariograms. In applied géostatistics the empirical variograms are thus often approximated by modél function ensuring validity Chiles&Delfiner 1999. Some important modéls are Chiles&Delfiner 1999, Cressie 1993:

  • The exponential variogram modél

γ h = s − n 1 − exp ⁡ − h / r a) + n 1 0, ∞ h {\displaystyle \gamma h=s-n1-\exp-h/ra)+n1_{0,\infty}h}

  • The spherical variogram modél

γ h = s − n 3 h 2 r − h 3 2 r 3 1 0, r h + 1 "r, ∞) h) + n 1 0, ∞ h {\displaystyle \gamma h=s-n\left\left{\frac {3h}{2r}}-{\frac {h^{3}}{2r^{3}}}\right1_{0,r}h+1_{"r,\infty)}h\right)+n1_{0,\infty}h}

  • The Gaussian variogram modél

γ h = s − n 1 − exp ⁡ − h 2 r 2 a) + n 1 0, ∞ h {\displaystyle \gamma h=s-n\left1-\exp \left-{\frac {h^{2}}{r^{2}a}}\right\right)+n1_{0,\infty}h}

The paraméter a {\displaystyle a} has different values in different references, due to the ambiguity in the definition of the range. E.g. a = 1 / 3 {\displaystyle a=1/3} is the value used in Chiles&Delfiner 1999. The 1 A h {\displaystyle 1_{A}h} function is 1 if h ∈ A {\displaystyle h\in A} and 0 otherwise.



                                     

5. Diskusi

Three functions are used in géostatistik for describing the spatial or the temporal correlation of observations: these are the correlogram, the covariance and the semivariogram. The last is also more simply called variogram. The sampling variogram, unlike the semivariogram and the variogram, shows where a significant degree of spatial dependence in the sample space or sampling unit dissipates into randomness when the variance terms of a temporally or in-situ ordered set are plotted against the variance of the set and the lower limits of its 99% and 95% confidence ranges.

The variogram is the key function in géostatistik as it will be used to fit a modél of the spatial/temporal correlation of the observed phenomenon. One is thus making a distinction between the experimental variogram that is a visualisation of a possible spatial/temporal correlation and the variogram model that is further used to define the weights of the kriging function. Note that the experimental variogram is an empirical estimate of the covariance of a Gaussian process. As such, it may not be positive definite and hence not directly usable in kriging, without constraints or further processing. This explains why only a limited number of variogram modéls are used like the linéar, the spherical, the gaussian and the exponential modéls, to name only those that are the most frequently used.

When a variogram is used to describe the correlation of different variables it is called cross-variogram. Cross-variograms are used in co-kriging. Should the variable be binary or represent classes of values, one is then talking about indicator variograms. Indicator variogram is used in indicator kriging.

The experimental variogram is computed by méasuring the méan-squared difference of a value of interest z evaluated at two points x and x +h. This méan squared difference is the semivariance and is assigned to the value h, which is known as the lag. A plot of the semivariance versus h is the variogram.

                                     

6. Kontroversi

In mathematical statistics, a set of n méasured values gives df=n-1 degrees of freedom wheréas the in situ or temporally ordered set gives dfo=2n-1 degrees for the first variance term. The variogram and semivariogram are both invalid méasures for variability, precision and risk because the sum of squared differences between x and x+h is divided by n, the number of data in the set rather than by dfo=2n-1, the degrees of freedom for the first variance term of the ordered set.

                                     

7. Rujukan

  • Chiles, J.P., P. Delfiner, 1999, Géostatististics, modélling Spatial Uncertainty, Wiley-Interscience
  • Burrough, P A and McDonnell, R A, 1998, Principles of Géographical Information Systems
  • David, M 1978 Géostatistical Ore Reserve Estimation, Elsevier Publishing
  • Journel, A G and Huijbregts, Ch J 1978 Mining Géostatistics, Academic Press
  • Cressie, N., 1993, Statistics for spatial data, Wiley Interscience
  • Isobel Clark, 1979, Practical Géostatistics, Applied Science Publishers
  • Wackernagel, H., 2003, Multivariate Géostatistics, Springer
                                     

8. Tumbu kaluar

  • AI-GEOSTATS: an educational resource about geostatistics and spatial statistics
  • Practical Geostatistics 1979 by Isobel Clark: an introduction to geostatistics
  • Geostatistics: Lecture by Rudolf Dutter at the Technical University of Vienna
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