GS+ – the premier geostatistical analysis program for desktop systems –
was introduced in 1988 as the first integrated geostatistics program for the PC.
It quickly became the geostatistics program of choice for users worldwide. Widely
praised, GS+ was the first geostatistics package to offer all components –
from variogram analysis through kriging and mapping – in an integrated
package that provides the flexibility demanded by the specialist and the simplicity
needed by the novice. GS+ runs on Windows XP, Vista, and Windows 7/8.
Variogram Analysis (Semivariance Analysis or Variography)
Comprehensive Semivariance Analysis provides both isotropic and anisotropic variograms.
You have complete control over separation interval classes – choose constant
interval classes or define different break points for every lag class. Anisotropic
directions can be individually targeted, and variograms can be scaled to sample
Variograms that appear in the Semivariance Analysis window – both isotropic
and anisotropic – can be enlarged into their own windows, from which values
and graphs can be printed, and from which each point along the curve can be decomposed
into the pairs of points on which it is based.
Variogram Surface Maps identify anisotropy quickly and accurately. Maps
of semivariance in every compass direction (the center marks the origin of each
variogram) allow the axis of maximum variation to be easily identified.
Dynamic Variogram Modeling – GS+ can calculate model parameters for 5 types
of models based on least squares (residuals) analysis, or individual model parameters
can be specified directly by the user.
Variance Cloud Analysis provides a graph of variance vs. separation distance for
every pair of points that make up a specific lag class. This allows outlying pairs
to be quickly identified and edited as needed.
h-Scattergram Analysis provides a graph of differences vs. separation distance for
every pair of points that make up a specific lag class. This is another way to quickly
identify outlying pairs and edit as needed.
The Variance by Pair listing provides variance values and separation distances for
each point in a specific variogram lag class.
GS+ provides four types of interpolation – Kriging, Cokriging, Conditional Simulation,
and Inverse Distance Weighting. Output is written to ASCII files that can be read
for mapping by GS+, ArcView®, or Surfer®.
Kriging provides optimal interpolation of points across a spatial domain for which
autocorrelation has been documented and measured with variograms. GS+ provides both
block and punctual kriging, and allows the user to choose the most appropriate variogram
model to use for the interpolation.
Cokriging is a type of kriging that allows one to better estimate map values using
a secondary variate sampled more intensely than the primary variate. If the primary
variate is difficult or expensive to measure, then cokriging can greatly improve
interpolation estimates without having to more intensely sample the primary variate.
Conditional Simulation provides optimal interpolation whereby measured data values
are honored at their locations. Other interpolation methods will smooth out local
details of spatial variation, which can be a problem when you are trying to map
sharp spatial boundaries such as contamination hotspots or fault lines.
Inverse Distance Weighting (IDW)
Inverse Distance Weighting (IDW) provides classical interpolation based on nearest
neighbor weighting. It is a simple interpolation method used in mapping programs
that do not use geostatistics, and assumes spatial dependence among points close
to one another (without measuring it).
The Interpolation Grid allows the user to define the boundaries of the interpolated
area and the intensity (grid spacing) at which the interpolation will proceed.
Polygon Outlines define irregular map boundaries and special areas to exclude from
kriging. An unlimited number of polygons can be defined by an unlimited number of
vertices (x-y boundary points).
Polygon Maps display the areas that will be included or excluded from kriging. Exclusive
and inclusive polygons are colored differently, and polygons can be nested within
Cross Validation Analysis
Cross Validation Analysis allows one to test different variogram models; bootstrapping
provides comparisons of the actual value of every point sampled vs. its estimated
value when removed from the data set.