3
<em>r.random.cells</em> generates a random sets of cells that are at
4
least <b>distance</b> apart. The cells are numbered from 1 to the
5
numbers of cells generated. Random cells will not be generated in
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<h3>Detailed parameter description</h3>
11
<dt><b>output</b></dt>
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<dd>Random cells. Each random cell has a unique non-zero cell value
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ranging from 1 to the number of cells generated. The heuristic for
14
this algorithm is to randomly pick cells until there are no cells
15
outside of the chosen cell's buffer of radius <b>distance</b>.</dd>
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<dt><b>distance</b></dt>
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<dd>Determines the minimum distance the centers of the random cells
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<dd>Specifies the random seed that
23
<em>r.random.cells</em> will use to generate the cells. If the random seed
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is not given,<em> r.random.cells</em> will get a seed from the process ID
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The original purpose for this program was to generate independent
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random samples of cells in a study area. The <b>distance</b> value is
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the amount of spatial autocorrelation for the map being studied.
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<!-- The amount of spatial autocorrelation can be determined by
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using <em>r.2Dcorrelogram</em> with
37
<em>r.2Dto1D</em>, or <em>r.1Dcorrelogram</em>. With <b>distance</b> set to
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zero, the <b>output</b> map will number each non-masked cell from 1 to the
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number of non-masked cells in the study region. -->
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North Carolina sample dataset example:
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<div class="code"><pre>
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g.region n=228500 s=215000 w=630000 e=645000 res=100 -p
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r.random.cells output=random_500m distance=500
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# optionally set 0 to NULL
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r.null random_500m setnull=0
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Random Field Software for GRASS GIS by Chuck Ehlschlaeger
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<p> As part of my dissertation, I put together several programs that help
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GRASS (4.1 and beyond) develop uncertainty models of spatial data. I hope
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you find it useful and dependable. The following papers might clarify their
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<li> Ehlschlaeger, C.R., Shortridge, A.M., Goodchild, M.F., 1997.
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Visualizing spatial data uncertainty using animation.
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Computers & Geosciences 23, 387-395. doi:10.1016/S0098-3004(97)00005-8</li>
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<li><a href="http://www.geo.hunter.cuny.edu/~chuck/paper.html">Modeling
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Uncertainty in Elevation Data for Geographical Analysis</a>, by
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Charles R. Ehlschlaeger, and Ashton M. Shortridge. Proceedings of the
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7th International Symposium on Spatial Data Handling, Delft,
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Netherlands, August 1996.</li>
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<li><a href="http://www.geo.hunter.cuny.edu/~chuck/acm/paper.html">Dealing
73
with Uncertainty in Categorical Coverage Maps: Defining, Visualizing,
74
and Managing Data Errors</a>, by Charles Ehlschlaeger and Michael
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Goodchild. Proceedings, Workshop on Geographic Information Systems at
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the Conference on Information and Knowledge Management, Gaithersburg
79
<li><a href="http://www.geo.hunter.cuny.edu/~chuck/gislis/gislis.html">Uncertainty
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in Spatial Data: Defining, Visualizing, and Managing Data
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Errors</a>, by Charles Ehlschlaeger and Michael
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Goodchild. Proceedings, GIS/LIS'94, pp. 246-253, Phoenix AZ,
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<a href="r.random.surface.html">r.random.surface</a>,
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<a href="r.random.html">r.random</a>
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Charles Ehlschlaeger; National Center for Geographic Information and
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Analysis, University of California, Santa Barbara.
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<p><i>Last changed: $Date: 2014-01-10 00:26:55 +0100 (Fri, 10 Jan 2014) $</i>