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@@ -9,7 +9,7 @@ using filter(s) of diameter distance. The influence of each random value on
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nearby cells is determined by a distance decay function based on exponent.
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If multiple filters are passed over the output maps, each filter is given a
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weight based on the weight inputs. The resulting random surface can have
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-"any" mean and variance, but the theoretical mean of an infinitely
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+<em>any</em> mean and variance, but the theoretical mean of an infinitely
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large map is 0.0 and a variance of 1.0. Description of the algorithm is in
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the <b>NOTES</b> section.
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@@ -17,7 +17,7 @@ the <b>NOTES</b> section.
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The random surface generated are composed of floating point numbers, and
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saved in the category description files of the output map(s). Cell values
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are uniformly or normally distributed between 1 and high values inclusive
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-(determined by whether the <em>-u</em> flag is used). The category names
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+(determined by whether the <b>-u</b> flag is used). The category names
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indicate the average floating point value and the range of floating point
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values that each cell value represents.
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@@ -26,68 +26,68 @@ values that each cell value represents.
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spatial error modeling. A procedure to use <em>r.random.surface</em> in
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spatial error modeling is given in the <b>NOTES</b> section.
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-<h3>Parameters:</h3>
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+<h3>Parameters</h3>
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<dl>
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-<dt><b>output</b>
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-<dd>Output map(s): Random surface(s). The cell values are a random
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-distribution between the low and high values inclusive. The category values
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-of the output map(s) are in the form "#.# #.# to #.#" where each
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-#.# is a floating point number. The first number is the average of the
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-random values the cell value represents. The other two numbers are the range
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-of random values for that cell value. The "average" mean value of
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-generated <tt>output</tt> map(s) is 0. The "average"
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-variance of map(s) generated is 1. The random values represent the standard
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-deviation from the mean of that random surface.
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-
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-<dt><b>distance</b>
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-<dd>Input value(s) [default 0.0]: distance determines the spatial dependence
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-of the output map(s). The distance value indicates the minimum distance at
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-which two map cells have no relationship to each other. A distance value of
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-0.0 indicates that there is no spatial dependence (i.e., adjacent cell
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-values have no relationship to each other). As the distance value increases,
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-adjacent cell values will have values closer to each other. But the range
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-and distribution of cell values over the output map(s) will remain the same.
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-Visually, the clumps of lower and higher values gets larger as distance
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-increases. If multiple values are given, each output map will have multiple
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-filters, one for each set of distance, exponent, and weight values.
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-
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-<dt><b>exponent</b>
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-<dd>Input value(s) [default 1.0]: exponent determines the distance decay
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-exponent for a particular filter. The exponent value(s) have the property of
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-determining the "texture" of the random surface. Texture will
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-decrease as the exponent value(s) get closer to 1.0. Normally, exponent will
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-be 1.0 or less. If there are no exponent values given, each filter will be
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-given an exponent value of 1.0. If there is at least one exponent value
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-given, there must be one exponent value for each distance value.
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-
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-<dt><b>flat</b>
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-<dd>Input value(s) [default 0.0]: flat determines the distance at which the
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-filter
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-
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-<dt><b>weight</b>
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-<dd>Input value(s) [default 1.0]: weight determines the relative importance
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-of each filter. For example, if there were two filters driving the algorithm
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-and weight=1.0, 2.0 was given in the command line: The second filter would
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-be twice as important as the first filter. If no weight values are given,
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-each filter will be just as important as the other filters defining the
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-random field. If weight values exist, there must be a weight value for each
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-filter of the random field.
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-
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-<dt><b>high</b>
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-<dd>Input value [default 255]: Specifies the high end of the range of cell
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-values in the output map(s). Specifying a very large high value will
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-minimize the "errors" caused by the random surface's
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-discretization. The word errors is in quotes because errors in
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-discretization are often going to cancel each other out and the spatial
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-statistics are far more sensitive to the initial independent random deviates
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-than any potential discretization errors.
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-
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-<dt><b>seed</b>
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-<dd>Input value(s) [default random]: Specifies the random seed(s), one for
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-each map, that <em>r.random.surface</em> will use to generate the initial
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-set of random values that the resulting map is based on. If the random seed
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-is not given, <em>r.random.surface</em> will get a seed from the process ID
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-number.
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+<dt><b>output</b></dt>
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+<dd>Random surface(s). The cell values are a random distribution
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+between the low and high values inclusive. The category values of the
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+output map(s) are in the form <em>#.# #.# to #.#</em> where each #.#
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+is a floating point number. The first number is the average of the
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+random values the cell value represents. The other two numbers are the
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+range of random values for that cell value. The <em>average</em> mean
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+value of generated <tt>output</tt> map(s) is 0. The <em>average</em>
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+variance of map(s) generated is 1. The random values represent the
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+standard deviation from the mean of that random surface.</dd>
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+
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+<dt><b>distance</b></dt>
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+<dd>Distance determines the spatial dependence of the output
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+map(s). The distance value indicates the minimum distance at which two
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+map cells have no relationship to each other. A distance value of 0.0
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+indicates that there is no spatial dependence (i.e., adjacent cell
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+values have no relationship to each other). As the distance value
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+increases, adjacent cell values will have values closer to each
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+other. But the range and distribution of cell values over the output
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+map(s) will remain the same. Visually, the clumps of lower and higher
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+values gets larger as distance increases. If multiple values are
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+given, each output map will have multiple filters, one for each set of
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+distance, exponent, and weight values.</dd>
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+
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+<dt><b>exponent</b></dt>
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+<dd>Exponent determines the distance decay exponent for a particular
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+filter. The exponent value(s) have the property of determining
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+the <em>texture</em> of the random surface. Texture will decrease as
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+the exponent value(s) get closer to 1.0. Normally, exponent will be
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+1.0 or less. If there are no exponent values given, each filter will
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+be given an exponent value of 1.0. If there is at least one exponent
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+value given, there must be one exponent value for each distance value.</dd>
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+
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+<dt><b>flat</b></dt>
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+<dd>Flat determines the distance at which the filter.</dd>
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+
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+<dt><b>weight</b></dt>
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+<dd>Weight determines the relative importance of each filter. For
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+example, if there were two filters driving the algorithm and
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+weight=1.0, 2.0 was given in the command line: The second filter would
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+be twice as important as the first filter. If no weight values are
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+given, each filter will be just as important as the other filters
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+defining the random field. If weight values exist, there must be a
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+weight value for each filter of the random field.</dd>
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+
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+<dt><b>high</b></dt>
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+<dd>Specifies the high end of the range of cell values in the output
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+map(s). Specifying a very large high value will minimize
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+the <em>errors</em> caused by the random surface's discretization. The
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+word errors is in quotes because errors in discretization are often
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+going to cancel each other out and the spatial statistics are far more
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+sensitive to the initial independent random deviates than any
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+potential discretization errors.</dd>
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+
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+<dt><b>seed</b></dt>
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+<dd>Specifies the random seed(s), one for each map,
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+that <em>r.random.surface</em> will use to generate the initial set of
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+random values that the resulting map is based on. If the random seed
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+is not given, <em>r.random.surface</em> will get a seed from the
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+process ID number.</dd>
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</dl>
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@@ -110,65 +110,56 @@ ignore the current mask for the same reason.
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<p>
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One of the most important uses for <em>r.random.surface</em> is to determine
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how the error inherent in raster maps might effect the analyses done with
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-those maps. If you wanted to check to see how sensitive your analysis is to
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-the errors in the DEMs in your study area, see:
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-
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-<p>"<a href="http://www.geo.hunter.cuny.edu/~chuck/CGFinal/paper.htm">Visualizing Spatial Data Uncertainty Using Animation (final draft)</a>," by Charles R. Ehlschlaeger, Ashton M. Shortridge, and Michael F. Goodchild. Submitted to Computers in GeoSciences in September, 1996, accepted October, 1996 for publication in June, 1997.
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-
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-<p>
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-"<a href="http://www.geo.hunter.cuny.edu/~chuck/SDH96/paper.html">Modeling Uncertainty in Elevation Data for Geographical Analysis</a>", by Charles R. Ehlschlaeger, and Ashton M. Shortridge. Proceedings of the 7th International Symposium on Spatial Data Handling, Delft, Netherlands, August 1996. </p>
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-
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-<p>
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-"<a href="http://www.geo.hunter.cuny.edu/~chuck/acm/paper.html">Dealing with Uncertainty in Categorical Coverage Maps: Defining, Visualizing, and Managing Data Errors</a>", by Charles Ehlschlaeger and Michael Goodchild. Proceedings, Workshop on Geographic Information Systems at the Conference on Information and Knowledge Management, Gaithersburg MD, 1994.
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-
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-<p>
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-"<a href="http://www.geo.hunter.cuny.edu/~chuck/gislis/gislis.html">Uncertainty in Spatial Data: Defining, Visualizing, and Managing Data Errors</a>", by Charles Ehlschlaeger and Michael Goodchild. Proceedings, GIS/LIS'94, pp. 246-253, Phoenix AZ,
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-1994.
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-
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-<p>
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-If you are interested in creating potential realizations of categorical
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-coverage maps, see <em>r.random.model</em>.
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-
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-<h2>SEE ALSO</h2>
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-
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-<em><a href="r.random.html">r.random</a>,
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-<a href="r.mapcalc.html">r.mapcalc</a>
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-</em>
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+those maps.
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<h2>REFERENCES</h2>
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-Random Field Software for GRASS by Chuck Ehlschlaeger<p>
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+Random Field Software for GRASS by Chuck Ehlschlaeger
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-<p>As part of my dissertation, I put together several programs that help
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+<p>
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+ 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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-use: </p>
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+use:
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-<p>"<a href="../../CGFinal/paper.htm">Visualizing Spatial Data
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-Uncertainty Using Animation (final draft)</a>," by Charles R.
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+<ul>
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+<li><a href="../../CGFinal/paper.htm">Visualizing Spatial Data
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+Uncertainty Using Animation (final draft)</a>, by Charles R.
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Ehlschlaeger, Ashton M. Shortridge, and Michael F. Goodchild. Submitted to
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Computers in GeoSciences in September, 1996, accepted October, 1996 for
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-publication in June, 1997. </p>
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-
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-<p>"<a href="http://www.geo.hunter.cuny.edu/~chuck/paper.html">Modeling Uncertainty in Elevation Data for
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-Geographical Analysis</a>", by Charles R. Ehlschlaeger, and Ashton M.
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-Shortridge. Proceedings of the 7th International Symposium on Spatial Data
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-Handling, Delft, Netherlands, August 1996. </p>
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+publication in June, 1997.</li>
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+
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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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+
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+<li><a href="http://www.geo.hunter.cuny.edu/~chuck/acm/paper.html">Dealing
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+with Uncertainty in Categorical Coverage Maps: Defining, Visualizing,
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+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
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+MD, 1994.</li>
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+
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+<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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+1994.</li>
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+</ul>
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-<p>"<a href="http://www.geo.hunter.cuny.edu/~chuck/acm/paper.html">Dealing with Uncertainty in
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-Categorical Coverage Maps: Defining, Visualizing, and Managing Data
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-Errors</a>", by Charles Ehlschlaeger and Michael Goodchild.
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-Proceedings, Workshop on Geographic Information Systems at the Conference on
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-Information and Knowledge Management, Gaithersburg MD, 1994. </p>
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-
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-<p>"<a href="http://www.geo.hunter.cuny.edu/~chuck/gislis/gislis.html">Uncertainty in Spatial Data:
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-Defining, Visualizing, and Managing Data Errors</a>", by Charles
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-Ehlschlaeger and Michael Goodchild. Proceedings, GIS/LIS'94, pp. 246-253,
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-Phoenix AZ, 1994. </p>
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+<h2>SEE ALSO</h2>
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+<em>
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+ <a href="r.random.html">r.random</a>,
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+ <a href="r.random.cell.html">r.random.cell</a>,
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+ <a href="r.mapcalc.html">r.mapcalc</a>
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+</em>
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<h2>AUTHORS</h2>
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Charles Ehlschlaeger, Michael Goodchild, and Chih-chang Lin; National Center
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for Geographic Information and Analysis, University of California, Santa
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Barbara.
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-<p><i>Last changed: $Date$</i>
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+<p>
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+<i>Last changed: $Date$</i>
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