1r.random.cells(1) Grass User's Manual r.random.cells(1)
2
3
4
6 r.random.cells - Generates random cell values with spatial dependence.
7
9 raster
10
12 r.random.cells
13 r.random.cells help
14 r.random.cells output=string distance=float [seed=integer] [--over‐
15 write]
16
17 Flags:
18 --overwrite
19
20 Parameters:
21 output=string
22 Name of indepent cells map
23
24 distance=float
25 Input value: max. distance of spatial correlation (value(s) >= 0.0)
26
27 seed=integer
28 Input value: random seed (SEED_MIN >= value >= SEED_MAX), default
29 [random]
30
32 r.random.cells generates a random sets of cells that are at least dis‐
33 tance apart. The cells are numbered from 1 to the numbers of cells gen‐
34 erated. Random cells will not be generated in areas masked off.
35
37 output Output map: Random cells. Each random cell has a unique non-
38 zero cell value ranging from 1 to the number of cells generated. The
39 heuristic for this algorithm is to randomly pick cells until there are
40 no cells outside of the chosen cell's buffer of radius distance.
41
42 distance Input value(s) [default 0.0]: distance determines the minimum
43 distance the centers of the random cells will be apart.
44
45 seed Input value [default: random]: Specifies the random seed that
46 r.random.cells will use to generate the cells. If the random seed is
47 not given, r.random.cells will get a seed from the process ID number.
48
50 The original purpose for this program was to generate independent ran‐
51 dom samples of cells in a study area. The distance value is the amount
52 of spatial autocorrelation for the map being studied. The amount of
53 spatial autocorrelation can be determined by using r.2Dcorrelogram with
54 r.2Dto1D, or r.1Dcorrelogram. With distance set to zero, the output map
55 will number each non-masked cell from 1 to the number of non-masked
56 cells in the study region.
57
59 Random Field Software for GRASS by Chuck Ehlschlaeger
60
61 As part of my dissertation, I put together several programs that help
62 GRASS (4.1 and beyond) develop uncertainty models of spatial data. I
63 hope you find it useful and dependable. The following papers might
64 clarify their use:
65
66 "Visualizing Spatial Data Uncertainty Using Animation (final draft),"
67 by Charles R. Ehlschlaeger, Ashton M. Shortridge, and Michael F. Good‐
68 child. Submitted to Computers in GeoSciences in September, 1996,
69 accepted October, 1996 for publication in June, 1997.
70
71 "Modeling Uncertainty in Elevation Data for Geographical Analysis", by
72 Charles R. Ehlschlaeger, and Ashton M. Shortridge. Proceedings of the
73 7th International Symposium on Spatial Data Handling, Delft, Nether‐
74 lands, August 1996.
75
76 "Dealing with Uncertainty in Categorical Coverage Maps: Defining, Visu‐
77 alizing, and Managing Data Errors", by Charles Ehlschlaeger and Michael
78 Goodchild. Proceedings, Workshop on Geographic Information Systems at
79 the Conference on Information and Knowledge Management, Gaithersburg
80 MD, 1994.
81
82 "Uncertainty in Spatial Data: Defining, Visualizing, and Managing Data
83 Errors", by Charles Ehlschlaeger and Michael Goodchild. Proceedings,
84 GIS/LIS'94, pp. 246-253, Phoenix AZ, 1994.
85
87 r.1Dcorrelogram, r.2Dcorrelogram, r.2Dto1D, r.random.surface, r.ran‐
88 dom.model, r.random
89
90
92 Charles Ehlschlaeger; National Center for Geographic Information and
93 Analysis, University of California, Santa Barbara.
94
95 Last changed: $Date: 2006/04/20 21:31:23 $
96
97 Full index
98
99
100
101GRASS 6.2.2 r.random.cells(1)