r.surf.idw(1)

NAME
r.surf.idw - Provides surface interpolation from raster point data by Inverse Distance Squared Weighting.

KEYWORDS
raster, surface, interpolation, IDW

SYNOPSIS
r.surf.idw
r.surf.idw --help
r.surf.idw [-e] input=name output=name [npoints=integer] [--overwrite] [--help] [--verbose] [--quiet] [--ui]

  Flags:

-e
    Output is the interpolation error

--overwrite
    Allow output files to overwrite existing files

--help
    Print usage summary

--verbose
    Verbose module output

--quiet
    Quiet module output

--ui
    Force launching GUI dialog

  Parameters:

input=name [required]
    Name of input raster map

output=name [required]
    Name for output raster map

npoints=integer
    Number of interpolation points
    Default: 12

DESCRIPTION
r.surf.idw fills a grid cell (raster) matrix with interpolated values generated from input raster data points. It uses a numerical approximation technique based on distance squared weighting of the values of nearest data points. The number of nearest data points used to determined the interpolated value of a cell can be specified by the user (default: 12 nearest data points).

If there is a current working mask, it applies to the output raster map. Only those cells falling within the mask will be assigned interpolated values. The search procedure for the selection of nearest neighboring points will consider all input data, without regard to the mask. The -e flag is the error analysis option that interpolates values only for those cells of the input raster map which have non-zero values and outputs the difference (see NOTES below).

The npoints parameter defines the number of nearest data points used to determine the interpolated value of an output raster cell.

NOTES
r.surf.idw is a surface generation utility which uses inverse distance squared weighting (as described in Applied Geostatistics by E. H. Isaaks and R. M. Srivastava, Oxford University Press, 1989) to assign interpolated values. The implementation includes a customized data structure somewhat akin to a sparse matrix which enhances the efficiency with which nearest data points are selected. For latitude/longitude projections, distances are calculated from point to point along a geodesic.

Unlike r.surf.idw2 (addon), which processes all input data points in each interpolation cycle, r.surf.idw attempts to minimize the number of input data for which distances must be calculated. Execution speed is therefore a function of the search effort, and does not increase appreciably with the number of input data points.

r.surf.idw will generally outperform r.surf.idw2 except when the input data layer contains few non-zero data, i.e. when the cost of the search exceeds the cost of the additional distance calculations performed by r.surf.idw2. The relative performance of these utilities will depend on the comparative speed of boolean, integer and floating point operations on a particular platform.

Worst case search performance by r.surf.idw occurs when the interpolated cell is located outside of the region in which input data are distributed. It therefore behooves the user to employ a mask when geographic region boundaries include large areas outside the general extent of the input data.

The degree of smoothing produced by the interpolation will increase relative to the number of nearest data points considered. The utility may be used with regularly or irregularly spaced input data. However, the output result for the former may include unacceptable nonconformities in the surface pattern.

The -e flag option provides a standard surface-generation error analysis facility. It produces an output raster map of the difference of interpolated values minus input values for those cells whose input data are non-zero. For each interpolation cycle, the known value of the cell under consideration is ignored, and the remaining input values are used to interpolate a result. The output raster map may be compared to the input raster map to analyze the distribution of interpolation error. This procedure may be helpful in choosing the number of nearest neighbors considered for surface generation.

SEE ALSO
r.surf.contour, r.surf.gauss, r.surf.fractal, r.surf.random, v.surf.idw, v.surf.rst

Overview: Interpolation and Resampling in GRASS GIS

AUTHOR
Greg Koerper
Global Climate Research Project
U.S. EPA Environmental Research Laboratory
200 S.W. 35th Street, JSB
Corvallis, OR 97333

Last changed: $Date: 2016-08-03 13:55:16 +0200 (Wed, 03 Aug 2016) $

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