Semivariance¶
Likewise, the term semivariance can be misleading, since the values shown in a variogram are entire variances of observations at a given spatial separation (lag).
Core Idea¶
Semivariance is treated here as the recurring crossdomainmodelsstructuresrepresentations identity summarized by this source-grounded definition: Likewise, the term semivariance can be misleading, since the values shown in a variogram are entire variances of observations at a given spatial separation (lag). A variogram is the graphical representation of the spatial dependence between pairs of data points, commonly used in geostatistics and spatial statistics. The term is sometimes used synonymously with semivariogram, but the latter is also used by some authors to refer to half of a variogram, and should therefore be avoided.
Scope of Application¶
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Definition. In practice it is impossible to sample everywhere, so the empirical variogram is used instead.
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Definition. The terms are used for all three forms of the function.
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Empirical variogram. In the case of empirical semivariogram, separation distance interval hk \pm \delta is used rather than exact distances, and usually isotropic conditions are assumed (i.e., that \gamma is only a.
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Documented setting. The variogram is the key function in geostatistics as it will be used to fit a model of the temporal/spatial correlation of the observed phenomenon.
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Documented setting. One is thus making a distinction between the experimental variogram that is a visualization of a possible spatial/temporal correlation and the variogram model that is further used to define the.
Clarity¶
A clear use of Semivariance names the carrier, the operative relation, and the conditions under which the source treats the identity as present. The minimal definition is Likewise, the term semivariance can be misleading, since the values shown in a variogram are entire variances of observations at a given spatial separation (lag).
Manages Complexity¶
Semivariance compresses multiple crossdomainmodelsstructuresrepresentations details into a stable diagnostic relation. The source shows both the central mechanism—if the process is furthermore isotropic, then the variogram and semivariogram can be represented by a function \gammai(h):=\gammas(h e1) of the distance h=|\mathbf{s}2-\mathbf{s}1| only (Cressie 1993).—and the practical consequence—if the variance V and correlation function c of a stationary process exist, they.
Abstract Reasoning¶
- Type the carrier. Identify the crossdomainmodelsstructuresrepresentations entities to which the claim applies.
- State the relation. Use the source-grounded identity: Likewise, the term semivariance can be misleading, since the values shown in a variogram are entire variances of observations at a given spatial separation (lag).
- Check operation and conditions. The semivariogram \gamma(h) was first defined by Matheron (1963) as half the average squared difference between a function and a translated copy of the function separated at distance h . 4.
Knowledge Transfer¶
Within the home domain. Knowledge about Semivariance transfers literally when a new case preserves the same carrier type, relation, and recognition test. In practice it is impossible to sample everywhere, so the empirical variogram is used instead. The terms are used for all three forms of the function. Beyond the home domain. No canonical parent is asserted for Semivariance. An outside case receives the specialist name only when the same typed roles and rejection conditions can be filled literally; otherwise the comparison remains an analogy pending later graph densification.
Neighborhood in Abstraction Space¶
Semivariance sits in a moderately populated region (50th percentile for distinctiveness): it has near-neighbors but no dense thicket of look-alikes.
Family — Unclustered & Miscellaneous (2551 abstractions)
Nearest neighbors
- Cross-spectrum — 0.87
- Big O in probability notation — 0.86
- Filling radius — 0.86
- Single Vegetative Obstruction Model — 0.86
- Score (statistics) — 0.86
Computed from structural-signature embeddings · 2026-10-08