r.random.cells(1)      Grass User's Manual	r.random.cells(1)



       r.random.cells - generates random sampling of cells.

       (GRASS Raster Program)

   SSYYNNOOPPSSIISS
       rr..rraannddoomm..cceellllss  rr..rraannddoomm..cceellllss  hheellpp  rr..rraannddoomm..cceellllss  [[--qq]]
       oouuttppuutt=name [[ddiissttaannccee=value]] [[sseeeedd=value]]

   DDEESSCCRRIIPPTTIIOONN
       r.random.cells generates a random sets of cells	that  are
       at  least distance apart. The cells are numbered from 1 to
       the numbers of cells generated. Random cells will  not  be
       generated in areas masked off.

   OOPPTTIIOONNSS
       -q  This  flag prevents the program from sending a verbose
       description to standard output  of  what  the  program  is
       doing.

       output  Output  map:  Random cells. Each random cell has a
       unique non-zero cell value ranging from 1 to the number of
       cells  generated.  The  heuristic for this algorithm is to
       randomly pick cells until there are no  cells  outside  of
       the chosen cell's buffer of radius distance.

       distance Input value(s) [default 0.0]: distance determines
       the minimum distance the centers of the random cells  will
       be apart.

       seed  Input  value [default: random]: Specifies the random
       seed that r.random.cells will use to generate  the  cells.
       If the random seed is not given, r.random.cells will get a
       seed from the process ID number.

   NNOOTTEESS
       The original purpose for  this  program	was  to  generate
       independent  random  samples of cells in a study area. The
       distance value is the amount  of  spatial  autocorrelation
       for  the map being studied. The amount of spatial autocor-
       relation can be determined by using  r.2Dcorrelogram  with
       r.2Dto1D,  or  r.1Dcorrelogram. With distance set to zero,
       the output map will number each non-masked cell from 1  to
       the number of non-masked cells in the study region.

RREEFFEERREENNCCEESS
       Random Field Software for GRASS by Chuck Ehlschlaeger



       As  part  of  my dissertation, I put together several pro-
       grams that help GRASS (4.1 and beyond) develop uncertainty
       models  of  spatial  data.  I  hope you find it useful and
       dependable. The following papers might clarify their use:

       "Visualizing  Spatial  Data  Uncertainty  Using	Animation
       (final  draft),"  by  Charles  R.  Ehlschlaeger, Ashton M.
       Shortridge, and Michael F. Goodchild. Submitted to Comput-
       ers  in	GeoSciences in September, 1996, accepted October,
       1996 for publication in June, 1997.

       "Modeling Uncertainty in Elevation Data	for  Geographical
       Analysis",  by  Charles	R.  Ehlschlaeger,  and	Ashton M.
       Shortridge. Proceedings of the 7th International Symposium
       on Spatial Data Handling, Delft, Netherlands, August 1996.

       "Dealing with Uncertainty in  Categorical  Coverage  Maps:
       Defining,  Visualizing,	and  Managing  Data  Errors",  by
       Charles Ehlschlaeger and Michael Goodchild.   Proceedings,
       Workshop  on Geographic Information Systems at the Confer-
       ence on Information and Knowledge Management, Gaithersburg
       MD, 1994.

       "Uncertainty  in  Spatial Data: Defining, Visualizing, and
       Managing Data Errors", by Charles Ehlschlaeger and Michael
       Goodchild.  Proceedings,  GIS/LIS'94, pp. 246-253, Phoenix
       AZ, 1994.

   SSEEEE AALLSSOO
       r.mask, r.1Dcorrelogram, r.2Dcorrelogram, r.2Dto1D, r.ran-
       dom.surface, r.random.model, r.random

   AAUUTTHHOORR
       Charles	 Ehlschlaeger;	National  Center  for  Geographic
       Information and Analysis, University of California,  Santa
       Barbara.

       _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.random.cells(1)
