Object/Class

geotrellis.vector.interpolation

EmpiricalVariogram

Related Docs: class EmpiricalVariogram | package interpolation

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object EmpiricalVariogram extends Serializable

This creates an empirical variogram from the dataset, which is then used to fit into one of the semivariogram ModelType for use in Kriging Interpolation

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  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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  3. final def ==(arg0: Any): Boolean

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  4. def apply(length: Int): EmpiricalVariogram

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  5. final def asInstanceOf[T0]: T0

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  6. def clone(): AnyRef

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  9. def finalize(): Unit

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  12. final def isInstanceOf[T0]: Boolean

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  13. def linear(pts: Array[PointFeature[Double]], radius: Option[Double] = None, lag: Double = 0.0): Array[(Double, Double)]

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    Computes empirical semivariogram for Linear model

  14. final def ne(arg0: AnyRef): Boolean

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  15. def nonlinear(pts: Array[PointFeature[Double]], maxdist: Double, binmax: Int): EmpiricalVariogram

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    Computes empirical semivariogram for Spherical, Gaussian, Exponential, Circular and Wave models

    Computes empirical semivariogram for Spherical, Gaussian, Exponential, Circular and Wave models

    pts

    PointFeature array for creating the variogram

    maxdist

    The bandwidth of variations that the empirical variogram is supposed to capture

    binmax

    The maximum number of bins in the empirical variogram to be created

  16. final def notify(): Unit

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  17. final def notifyAll(): Unit

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  18. final def synchronized[T0](arg0: ⇒ T0): T0

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  19. def toString(): String

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  20. final def wait(): Unit

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