1r.random.cells(1) Grass User's Manual r.random.cells(1)
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6 r.random.cells - Generates random cell values with spatial dependence.
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9 raster
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12 r.random.cells
13 r.random.cells help
14 r.random.cells output=string distance=float [seed=integer] [--over‐
15 write] [--verbose] [--quiet]
16 Flags:
18 --overwrite
19 Allow output files to overwrite existing files
20 --verbose
22 Verbose module output
23 --quiet
25 Quiet module output
26 Parameters:
28 output=string
29 Name of indepent cells map
30 distance=float
32 Input value: max. distance of spatial correlation (value(s) >= 0.0)
33 seed=integer
35 Input value: random seed (SEED_MIN >= value >= SEED_MAX), default
36 [random]
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39 r.random.cells generates a random sets of cells that are at least dis‐
40 tance apart. The cells are numbered from 1 to the numbers of cells gen‐
41 erated. Random cells will not be generated in areas masked off.
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44 output Output map: Random cells. Each random cell has a unique non-
45 zero cell value ranging from 1 to the number of cells generated. The
46 heuristic for this algorithm is to randomly pick cells until there are
47 no cells outside of the chosen cell's buffer of radius distance.
48 distance Input value(s) [default 0.0]: distance determines the minimum
50 distance the centers of the random cells will be apart.
51 seed Input value [default: random]: Specifies the random seed that
53 r.random.cells will use to generate the cells. If the random seed is
54 not given, r.random.cells will get a seed from the process ID number.
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57 The original purpose for this program was to generate independent ran‐
58 dom samples of cells in a study area. The distance value is the amount
59 of spatial autocorrelation for the map being studied. The amount of
60 spatial autocorrelation can be determined by using r.2Dcorrelogram with
61 r.2Dto1D, or r.1Dcorrelogram. With distance set to zero, the output map
62 will number each non-masked cell from 1 to the number of non-masked
63 cells in the study region.
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66 Random Field Software for GRASS by Chuck Ehlschlaeger
67 As part of my dissertation, I put together several programs that help
69 GRASS (4.1 and beyond) develop uncertainty models of spatial data. I
70 hope you find it useful and dependable. The following papers might
71 clarify their use:
72 "Visualizing Spatial Data Uncertainty Using Animation (final draft),"
74 by Charles R. Ehlschlaeger, Ashton M. Shortridge, and Michael F. Good‐
75 child. Submitted to Computers in GeoSciences in September, 1996,
76 accepted October, 1996 for publication in June, 1997.
77 "Modeling Uncertainty in Elevation Data for Geographical Analysis", by
79 Charles R. Ehlschlaeger, and Ashton M. Shortridge. Proceedings of the
80 7th International Symposium on Spatial Data Handling, Delft, Nether‐
81 lands, August 1996.
82 "Dealing with Uncertainty in Categorical Coverage Maps: Defining, Visu‐
84 alizing, and Managing Data Errors", by Charles Ehlschlaeger and Michael
85 Goodchild. Proceedings, Workshop on Geographic Information Systems at
86 the Conference on Information and Knowledge Management, Gaithersburg
87 MD, 1994.
88 "Uncertainty in Spatial Data: Defining, Visualizing, and Managing Data
90 Errors", by Charles Ehlschlaeger and Michael Goodchild. Proceedings,
91 GIS/LIS'94, pp. 246-253, Phoenix AZ, 1994.
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94 r.1Dcorrelogram, r.2Dcorrelogram, r.2Dto1D, r.random.surface, r.ran‐
95 dom.model, r.random
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99 Charles Ehlschlaeger; National Center for Geographic Information and
100 Analysis, University of California, Santa Barbara.
101 Last changed: $Date: 2006-04-20 23:31:24 +0200 (Thu, 20 Apr 2006) $
103 Full index
105 © 2003-2008 GRASS Development Team
107GRASS 6.3.0 r.random.cells(1)