ArcGIS for Desktop Extensions

ArcGIS Geostatistical Analyst

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Model

Interpolation

Create surfaces from sample data using these interpolation methods:

  • Inverse distance weighted
  • Radial-based functions, which include the following kernels
    • Thin plate spline
    • Spline with tension
    • Multiquadratic
    • Inverse multiquadratic
    • Completely regularized spline kernels
  • Global and local polynomials
  • Kriging for exact data and for error-contaminated data
    • Ordinary, for data with unknown constant mean value
    • Simple, for data with known mean value
    • Universal, for data with mean value as a function on coordinates
    • Indicator, for discrete data or data transformed to discrete
    • Probability, for discrete data as primary variable and continuous data as secondary variables
    • Disjunctive, for nonlinear predictions
  • Cokriging (multivariate version of the above-mentioned kriging models)
  • Isotropical or anisotropical models

Kriging Output Surface Types

  • Prediction
  • Prediction standard error (measure of the prediction quality)
  • Probability map (probability that specified threshold value is exceeded)
  • Error of indicators (measure of the probability map uncertainty)
  • Quantile map (over- and underpredicted values)

Modeling Tools for Kriging

  • Data transformations
    • Box–Cox
    • Logarithmic
    • Arcsine
    • Normal score
  • Data detrending
    • Global polynomial
    • Local polynomial
  • Variography
    • Models (four can be used simultaneously)
      • Nugget
      • Circular
      • Spherical
      • Tetraspherical
      • Pentaspherical
      • Exponential
      • Gaussian
      • Rational quadratic
      • Hole effect
      • K-Bessel
      • J-Bessel
      • Stable
    • Semivariogram/Covariance surface
    • Anisotropy
    • Specifying or estimating the proportion of measurement error in the nugget
    • Cross-covariance option for shift between variables
    • Estimation of all or part of the model parameters by a modified weighted least squares algorithm
  • Declustering
    • Cell
    • Polygonal
  • Checking for data bivariate distribution

Searching Neighborhood

To select neighboring data to predict the value for the target point

  • Ellipse with one, four, or eight angular sectors
  • Minimum and maximum number of points in each sector

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