r.surf.idw(1)	       Grass User's Manual	    r.surf.idw(1)



NNAAMMEE
       rr..ssuurrff..iiddww  - Surface interpolation utility for raster map
       layers.

SSYYNNOOPPSSIISS
       rr..ssuurrff..iiddww [--ee] iinnppuutt==_n_a_m_e oouuttppuutt==_n_a_m_e [nnppooiinnttss==_v_a_l_u_e]

DDEESSCCRRIIPPTTIIOONN
       _r_._s_u_r_f_._i_d_w fills a grid cell (raster) matrix with interpo-
       lated  values  generated  from  a  set of input layer 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 speci-
       fied by the user (default: 12 nearest data points).


       If there is a current working mask, it applies to the out-
       put  raster file. Only those cells falling within the mask
       will be assigned interpolated values. The search procedure
       for  the selection of nearest neighboring points will con-
       sider all input data, without regard to the mask.

OOPPTTIIOONNSS
       The command line input is as follows:

   FFllaagg::
       --ee     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).


   PPaarraammeetteerrss::
       iinnppuutt==_n_a_m_e
	      Name  of	an  input  raster map layer containing an
	      incomplete set of data  values.  (i.e.,  some  grid
	      cells contain known data values while the rest con-
	      tain zero (0)).

       oouuttppuutt==_n_a_m_e
	      Name to be assigned to new output raster	map  that
	      represents  the  surface	generated  from the known
	      data values in the input layer.

       nnppooiinnttss==_v_a_l_u_e
	      Number of nearest data points used to determine the
	      interpolated value of an output raster cell.
	      Default: 12


NNOOTTEESS
       _r_._s_u_r_f_._i_d_w  is  a  surface  generation  utility which uses
       inverse	distance  squared  weighting  (as  described   in
       AApppplliieedd	GGeeoossttaattiissttiiccss  by E. H. Isaaks and R. M.  Srivas-
       tava, Oxford University Press, 1989)  to  assign  interpo-
       lated  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_._s_u_r_f_._i_d_w_2, which processes all input data points
       in each interpolation cycle, _r_._s_u_r_f_._i_d_w attempts to  mini-
       mize  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_._s_u_r_f_._i_d_w will generally  outperform  _r_._s_u_r_f_._i_d_w_2  except
       when the input data layer contains few non-zero data, i.e.
       when the cost of the search exceeds the cost of the  addi-
       tional  distance  calculations  performed  by _r_._s_u_r_f_._i_d_w_2.
       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_._s_u_r_f_._i_d_w  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 bound-
       aries 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 --ee flag option provides a standard  surface-generation
       error  analysis facility. It produces an output raster map
       of the difference of interpolated values minus input  val-
       ues  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 con-
       sidered for surface generation.

SSEEEE AALLSSOO
       _r_._m_a_s_k

       _r_._s_u_r_f_._c_o_n_t_o_u_r

       _r_._s_u_r_f_._i_d_w_2

       _s_._s_u_r_f_._i_d_w

       _s_._s_u_r_f_._r_s_t

       _r_._s_u_r_f_._g_a_u_s_s

       _r_._s_u_r_f_._f_r_a_c_t_a_l

       _r_._s_u_r_f_._r_a_n_d_o_m

       _p_a_r_s_e_r

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

       _L_a_s_t _c_h_a_n_g_e_d_: _$_D_a_t_e_: _2_0_0_2_/_0_1_/_2_5 _0_5_:_4_5_:_3_4 _$



GRASS 5.0					    r.surf.idw(1)
