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  dis-
       tance apart. The cells are numbered from 1 to the numbers of cells gen-
       erated. 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 heuris-
       tic  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 ran-
       dom 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 autocorrelation 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 programs 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. Good-
       child. Submitted  to  Computers	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, Nether-
       lands, August 1996.

       "Dealing with Uncertainty in Categorical Coverage Maps: Defining, Visu-
       alizing, and Managing Data Errors", by Charles Ehlschlaeger and Michael
       Goodchild.  Proceedings, Workshop on Geographic Information Systems  at
       the  Conference	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.random.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_: _$_D_a_t_e_: _2_0_0_3_/_0_2_/_0_9 _1_2_:_0_7_:_2_3 _$



GRASS 5.0						     r.random.cells(1)
