r.random.surface(1)

📦
version 8.4.2 · 8 versions
, in fedora 44

💖 r.random.surface — the manual, but make it make sense 💖

okay bestie, gather round, because we're talking about r.random.surface, a GRASS GIS module that generates random surface(s) with spatial dependence. yes it's giving chaos but make it organized chaos. 🦋

✨ KEYWORDS ✨

raster, surface, random — that's the vibe check, she passed. 💅

🌸 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 — makes your cell values uniformly distributed. no cap.
  • --overwrite — lets the output files just steamroll over existing files. rude but allowed.
  • --help — prints the usage summary, aka the cheat sheet.
  • --verbose — verbose module output, she will not stop talking 💬
  • --quiet — quiet module output, she's giving silent treatment 🤫
  • --ui — force launches the GUI dialog, for when you want buttons instead of typing.

🧚 Parameters

  • output=string[,string,...] — [required] the name(s) for your output raster map(s). you must give her this, periodt.
  • distance=float — maximum distance of spatial correlation, must be >= 0.0. Default: 0.0.
  • exponent=float — distance decay exponent, must be > 0.0. Default: 1.0.
  • flat=float — the distance filter stays flat before beginning the exponent. Default: 0.0.
  • seed=integer — random seed, default [random].
  • high=integer — maximum cell value of the distribution. Default: 255.

👑 DESCRIPTION 👑

So. r.random.surface generates a spatially dependent random surface. The random surface is composed of values that represent the deviation from the mean of the initial random values driving the algorithm. Those initial random values? They're independent Gaussian random deviates with a mean of 0 and a standard deviation of 1. Obsessed with her honestly.

Those initial values get spread over each output map using filter(s) of diameter distance. How much each random value influences nearby cells is decided by a distance decay function based on exponent. And if multiple filters get passed over the output maps, each filter gets 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. The full description of the algorithm lives in the NOTES section (scroll down bestie, it's all there 🫶).

The random surfaces generated are composed of floating point numbers, and they're saved in the category description files of the output map(s). Cell values are uniformly or normally distributed between 1 and high values inclusive — that's 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 whole procedure to use r.random.surface in spatial error modeling is given in the NOTES section. iconic behavior tbh.

💜 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. so basically she's got layers, iykyk 💅

🌈 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 get 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. matching sets only, no mix-and-match 💖

🌸 flat

flat determines the distance at which the filter.

yes that's the whole sentence. she's a woman of few words. we respect it. 🎀

💀 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. she said what she said. 💅

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. (yes, she ignores the mask. bold. iconic. on purpose.)

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.

🎀 SEE ALSO 🎀

r.random, r.random.cells, r.mapcalc, r.surf.random

go read those next bestie, it's a whole cinematic universe 🌸

👑 AUTHORS 👑

Charles Ehlschlaeger, Michael Goodchild, and Chih-chang Lin; National Center for Geographic Information and Analysis, University of California, Santa Barbara. absolute legends, all of them. 💖

🌟 SOURCE CODE 🌟

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

Accessed: Saturday Jan 17 05:40:57 2026

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