Ubuntu Manpage: r.random.surface - Generates random surface(s) with spatial dependence.

Provided by: grass-doc_8.3.2-1ubuntu2_all

NAME

       r.random.surface  - Generates random surface(s) with spatial dependence.

KEYWORDS

       raster, surface, random

SYNOPSIS

       r.random.surface
       r.random.surface --help
       r.random.surface  [-u]  output=string[,string,...]   [distance=float]    [exponent=float]    [flat=float]
       [seed=integer]   [high=integer]   [--overwrite]  [--help]  [--verbose]  [--quiet]  [--ui]

   Flags:
       -u
           Uniformly distributed cell values

       --overwrite
           Allow output files to overwrite existing files

       --help
           Print usage summary

       --verbose
           Verbose module output

       --quiet
           Quiet module output

       --ui
           Force launching GUI dialog

   Parameters:
       output=string[,string,...] [required]
           Name for output raster map(s)

       distance=float
           Maximum distance of spatial correlation (value >= 0.0)
           Default: 0.0

       exponent=float
           Distance decay exponent (value > 0.0)
           Default: 1.0

       flat=float
           Distance filter remains flat before beginning exponent
           Default: 0.0

       seed=integer
           Random seed, default [random]

       high=integer
           Maximum cell value of distribution
           Default: 255

DESCRIPTION

r.random.surface generates a spatially dependent random surface. The random surface is composed of
values representing the deviation from the mean of the initial random values driving the algorithm. The
initial random values are independent Gaussian random deviates with a mean of 0 and standard deviation of
1. The initial values are spread over each output map using filter(s) of diameter distance. The
influence of each random value on nearby cells is determined by a distance decay function based on
exponent. If multiple filters are passed over the output maps, each filter is given a weight based on
the weight inputs. The resulting random surface can have any mean and variance, but the theoretical mean
of an infinitely large map is 0.0 and a variance of 1.0. Description of the algorithm is in the NOTES
section.

The random surface generated are composed of floating point numbers, and saved in the category
description files of the output map(s). Cell values are uniformly or normally distributed between 1 and
high values inclusive (determined by whether the -u flag is used). The category names indicate the
average floating point value and the range of floating point values that each cell value represents.

r.random.surface’s original goal is to generate random fields for spatial error modeling. A procedure to
use r.random.surface in spatial error modeling is given in the NOTES section.

Detailed parameter description
output
Random surface(s). The cell values are a random distribution between the low and high values
inclusive. The category values of the output map(s) are in the form #.# #.# to #.# where each #.# is
a floating point number. The first number is the average of the random values the cell value
represents. The other two numbers are the range of random values for that cell value. The average
mean value of generated output map(s) is 0. The average variance of map(s) generated is 1. The random
values represent the standard deviation from the mean of that random surface.

distance
Distance determines the spatial dependence of the output map(s). The distance value indicates the
minimum distance at which two map cells have no relationship to each other. A distance value of 0.0
indicates that there is no spatial dependence (i.e., adjacent cell values have no relationship to
each other). As the distance value increases, adjacent cell values will have values closer to each
other. But the range and distribution of cell values over the output map(s) will remain the same.
Visually, the clumps of lower and higher values gets larger as distance increases. If multiple values
are given, each output map will have multiple filters, one for each set of distance, exponent, and
weight values.

exponent
Exponent determines the distance decay exponent for a particular filter. The exponent value(s) have
the property of determining the texture of the random surface. Texture will decrease as the exponent
value(s) get closer to 1.0. Normally, exponent will be 1.0 or less. If there are no exponent values
given, each filter will be given an exponent value of 1.0. If there is at least one exponent value
given, there must be one exponent value for each distance value.

flat
Flat determines the distance at which the filter.

weight
Weight determines the relative importance of each filter. For example, if there were two filters
driving the algorithm and weight=1.0, 2.0 was given in the command line: The second filter would be
twice as important as the first filter. If no weight values are given, each filter will be just as
important as the other filters defining the random field. If weight values exist, there must be a
weight value for each filter of the random field.

high
Specifies the high end of the range of cell values in the output map(s). Specifying a very large high
value will minimize the errors caused by the random surface’s discretization. The word errors is in
quotes because errors in discretization are often going to cancel each other out and the spatial
statistics are far more sensitive to the initial independent random deviates than any potential
discretization errors.

seed
Specifies the random seed(s), one for each map, that r.random.surface will use to generate the
initial set of random values that the resulting map is based on. If the random seed is not given,
r.random.surface will get a seed from the process ID number.

NOTES

       While most literature uses the term random  field  instead  of  random  surface,  this  algorithm  always
       generates a surface. Thus, its use of random surface.

       r.random.surface builds the random surface using a filter algorithm smoothing a map of independent random
       deviates.  The  size of the filter is determined by the largest distance of spatial dependence. The shape
       of the filter is determined by the distance decay exponent(s), and the various weights if different  sets
       of  spatial  parameters  are used. The map of independent random deviates will be as large as the current
       region PLUS the extent of the filter. This will eliminate edge effects caused by the reduction of degrees
       of freedom. The map of independent random deviates will ignore the current mask for the same reason.

       One of the most important uses for r.random.surface is to determine how the error inherent in raster maps
       might effect the analyses done with those maps.

EXAMPLE

       Generate a random surface (using extent of North Carolina sample dataset):
       g.region raster=elevation res=100 -p
       r.surf.random output=randomsurf min=10 max=100
       # verify distribution
       r.univar -e map=randomsurf
       Figure: Random surface example (min: 10; max: 100)

       With the histogram tool the cell values versus count can be shown.

       Figure: Histogram of random surface example (min: 10; max: 100)

REFERENCES

       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:

           •   Ehlschlaeger,  C.R.,  Shortridge,  A.M.,  Goodchild,  M.F.,  1997.   Visualizing   spatial   data
               uncertainty      using      animation.       Computers     &     Geosciences     23,     387-395.
               doi:10.1016/S0098-3004(97)00005-8

           •   Ehlschlaeger,  C.R.,  Shortridge,  A.M.,  1996.   Modeling  Uncertainty  in  Elevation  Data  for
               Geographical  Analysis.  Proceedings of the 7th International Symposium on Spatial Data Handling,
               Delft, Netherlands, August 1996.

           •   Ehlschlaeger, C.R., Goodchild, M.F., 1994.  Dealing  with  Uncertainty  in  Categorical  Coverage
               Maps:  Defining,  Visualizing,  and  Managing  Data  Errors.  Proceedings, Workshop on Geographic
               Information Systems at the Conference on Information and Knowledge Management,  Gaithersburg  MD,
               1994.

           •   Ehlschlaeger,  C.R.,  Goodchild, M.F., 1994.  Uncertainty in Spatial Data: Defining, Visualizing,
               and Managing Data Errors. Proceedings, GIS/LIS’94, pp. 246-253, Phoenix AZ, 1994.

AUTHORS

       Charles Ehlschlaeger, Michael Goodchild, and Chih-chang Lin; National Center for  Geographic  Information
       and Analysis, University of California, Santa Barbara

SOURCE CODE

       Available at: r.random.surface source code (history)

       Accessed: Monday Apr 01 03:07:50 2024

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       © 2003-2024 GRASS Development Team, GRASS GIS 8.3.2 Reference Manual

GRASS 8.3.2                                                                             r.random.surface(1grass)