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@@ -2,7 +2,8 @@
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The <em>r.in.xyz</em> module will load and bin ungridded x,y,z ASCII data
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into a new raster map. The user may choose from a variety of statistical
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-methods in creating the new raster.
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+methods in creating the new raster. Gridded data provided as a stream of
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+x,y,z points may also be imported.
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<p>
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<em>r.in.xyz</em> is designed for processing massive point cloud datasets,
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@@ -39,6 +40,26 @@ Available statistics for populating the raster are:<br>
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<h2>NOTES</h2>
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+<h4>Gridded data</h4>
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+
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+If data is known to be on a regular grid <em>r.in.xyz</em> can reconstruct
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+the map perfectly as long as some care is taken to set up the region
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+correctly and that the data's native map projection is used. A typical
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+method would involve determining the grid resolution either by examining
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+the data's associated documentation or by studying the text file. Next scan
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+the data with <em>r.in.xyz</em>'s <b>-s</b> (or <b>-g</b>) flag to find the
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+input data's bounds. GRASS uses the cell-center raster convention where
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+data points fall within the center of a cell, as opposed to the grid-node
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+convention. Therefore you will need to grow the region out by half a cell
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+in all directions beyond what the scan found in the file. After the region
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+bounds and resolution are set correctly with <em>g.region</em>, run
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+<em>r.in.xyz</em> using the <i>n</i> method and verify that n=1 at all places.
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+<em>r.univar</em> can help. Once you are confident that the region exactly
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+matches the data proceed to run <em>r.in.xyz</em> using one of the <i>mean,
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+min, max</i>, or <i>median</i> methods. With n=1 throughout, the result
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+should be identical regardless of which of those methods are used.
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+
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+
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<h4>Memory use</h4>
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While the <b>input</b> file can be arbitrarily large, <em>r.in.xyz</em>
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Hamish Bowman