TGIS_InterpolationKriging class

DK11 for Delphi | GisInterpolation.TGIS_InterpolationKriging | Classes | Constructors | Methods | Properties

Implementation of the ordinary Kriging interpolation method.

Available also on: .NET | Java | ActiveX | Python.

// Delphi
type
  TGIS_InterpolationKriging = class( TGIS_VectorToGridAbstract )
  end;

// C++ Builder
class PASCALIMPLEMENTATION TGIS_InterpolationKriging : public TGIS_VectorToGridAbstract
{
};

→ TGIS_BaseObjectDisposable → TGIS_VectorToGridAbstract

The Kriging interpolation method is a geostatistical technique that considers the distance and the degree of variation of the input data.

There are two different scenarios for generating a Kriging interpolated grid:

1. based on a Z, M, or Z+M coordinate value:
   • set the Coordinate property to the desired option
   • pass an empty string as the field name to the Generate method
2. based on an attribute field value:
   • pass the field name to the Generate method
   • with the above, the Coordinate property is neglected

The Semivariance property defines semivariance model implementation. The default semivariance model is Power Law. The complete list of implemented semivariance models:

• Power Law as TGIS_SemivariancePowerLaw
• Exponential as TGIS_SemivarianceExponential
• Gaussian as TGIS_SemivarianceGaussian
• Spherical as TGIS_SemivarianceSpherical
• Circular as TGIS_SemivarianceCircular
• Linear as TGIS_SemivarianceLinear

To implement an additional semivariance model, inherit from TGIS_SemivarianceAbstract, TGIS_SemivarianceWithRange or TGIS_SemivarianceWithSill (depending on the type of semivariance).

Use a windowed version of the algorithm (Windowed=True) for large datasets (thousands of points and more). For up to few hundred points, the non-windowed (full sample) algorithm is efficient enough and gives best possible results. The Radius property is the size of the window in map units. The MinPoints/MaxPoints properties define the minimum/maximum number of sample points necessary/taken to interpolate a grid value.

• interpolated values exceed the range of minimum to maximum values of the input data
• extreme input value changes on short distances do not cause huge errors

• interpolated values do not match the input values (but are very close)

var
  src : TGIS_LayerVector ;
  dst : TGIS_LayerPixel ;
  vtg : TGIS_InterpolationKriging ;  
  ext : TGIS_Extent ; 
begin
  // get the source layer (GIS is TGIS_ViewerWnd)
  src := TGIS_LayerVector( GIS.Get( 'points' ) ) ;
  ext := src.Extent ;
 
  // create the destination layer (grid, 200x200 pixels)
  dst := TGIS_LayerPixel.Create ;
  dst.Build( True, lsrc.CS, ext, 200, 200 ) ;
  dst.Name := 'Heatmap' ;
  dst.Params.Pixel.GridShadow := False ;
 
  // create an instance of the kriging interpolation engine
  vtg := TGIS_InterpolationKriging.Create ;
  try
    // attach event to track the progress
    vtg.BusyEvent := doBusyEvent ;
 
    // set the semivariance model
    svr := TGIS_SemivarianceSpherical.Create ;
    svr.Range := 15.0 ;
    svr.Sill := 0.0 ;
    vtg.Semivariance := svr ;
 
    // initiate the grid generation process for the
    // POPULATION attribute field
    vtg.Import( lsrc, ext, 'POPULATION', ldst, ext ) ;
  finally
    vtg.Free ;
  end ;  
 
  // add the grid to the viewer
  GIS.Add( dst ) ;
 
  // update the viewer
  GIS.FullExtent ;
end ;

• Interpolation sample
• JupyterNotebookDKForPython sample