Key Features
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
- 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)
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