Key Features

• Exploratory spatial data analysis tools
• Random training and testing data subsets creation
• Renderers
• Export result of predictions
• Searching neighborhood for selecting local neighboring data for prediction to target point
• Kriging output surface types
• Modeling tools for kriging
• Diagnostics

Exploratory spatial data analysis tools

• Histogram and summary statistics
• Normal quantile–quantile plot
• Trend analysis
• Semivariogram/Covariance cloud and map
• Voronoi map
• General quantile–quantile plot
• Cross-Covariance cloud and map

Random training and testing data subsets creation

Interpolation methods. All models can be isotropical or anisotropical. There is no restriction on maximum number of input data.

• 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 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)

Renderers

• Contours (isolines)
• Filled contours
• Regular grid (All models allow data averaging in each cell; block interpolation.)
• Hillshading

Export result of predictions to

• Contour lines
• Polygons
• Grid
• Specified point locations
• Geostatistical layer that stores the model parameters from the interpolation and renderers

Searching neighborhood for selecting local neighboring data for prediction to target point

• Ellipse with four or eight angular sectors, or without sectors, with specified minimum and maximum number of points in each sector of the elliptical moving window

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

Diagnostics

• Cross-validation for checking the model's quality
• Validation for checking prediction quality
• Cross-validation comparison of several models
• Show predicted value at cursor (MapTips)