Provided by: grass-doc_7.8.2-1build3_all bug

NAME   -  Creates a raster map from an assemblage of many coordinates using univariate


       raster, import, statistics, conversion, aggregation, binning, ASCII, LIDAR

SYNOPSIS --help   [-sgi]   input=name   output=name    [method=string]      [separator=character]
       [x=integer]       [y=integer]       [z=integer]       [skip=integer]      [zrange=min,max]
       [zscale=float]        [value_column=integer]        [vrange=min,max]        [vscale=float]
       [type=string]   [percent=integer]   [pth=integer]   [trim=float]   [--overwrite]  [--help]
       [--verbose]  [--quiet]  [--ui]

           Scan data file for extent then exit

           In scan mode, print using shell script style

           Ignore broken lines

           Allow output files to overwrite existing files

           Print usage summary

           Verbose module output

           Quiet module output

           Force launching GUI dialog

       input=name [required]
           ASCII file containing input data (or "-" to read from stdin)

       output=name [required]
           Name for output raster map

           Statistic to use for raster values
           Options:  n,  min,  max,  range,  sum,  mean,  stddev,  variance,  coeff_var,  median,
           percentile, skewness, trimmean
           Default: mean
           n: Number of points in cell
           min: Minimum value of point values in cell
           max: Maximum value of point values in cell
           range: Range of point values in cell
           sum: Sum of point values in cell
           mean: Mean (average) value of point values in cell
           stddev: Standard deviation of point values in cell
           variance: Variance of point values in cell
           coeff_var: Coefficient of variance of point values in cell
           median: Median value of point values in cell
           percentile: Pth (nth) percentile of point values in cell
           skewness: Skewness of point values in cell
           trimmean: Trimmed mean of point values in cell

           Field separator
           Special characters: pipe, comma, space, tab, newline
           Default: pipe

           Column number of x coordinates in input file (first column is 1)
           Default: 1

           Column number of y coordinates in input file
           Default: 2

           Column number of data values in input file
           If  a separate value column is given, this option refers to the z-coordinate column to
           be filtered by the zrange option
           Default: 3

           Number of header lines to skip at top of input file
           Default: 0

           Filter range for z data (min,max)

           Scale to apply to z data
           Default: 1.0

           Alternate column number of data values in input file
           If not given (or set to 0) the z-column data is used
           Default: 0

           Filter range for alternate value column data (min,max)

           Scale to apply to alternate value column data
           Default: 1.0

           Type of raster map to be created
           Storage type for resultant raster map
           Options: CELL, FCELL, DCELL
           Default: FCELL
           CELL: Integer
           FCELL: Single precision floating point
           DCELL: Double precision floating point

           Percent of map to keep in memory
           Options: 1-100
           Default: 100

           Pth percentile of the values
           Options: 1-100

           Discard <trim> percent of the smallest and <trim> percent of the largest observations
           Options: 0-50


       The module will load and bin ungridded x,y,z ASCII data into a  new  raster  map.
       The  user  may  choose  from  a variety of statistical methods in creating the new raster.
       Gridded data provided as a stream of x,y,z points may also be imported.

       Please note that the current region extents and resolution are used for the import. It  is
       therefore  recommended  to first use the -s flag to get the extents of the input points to
       be imported, then adjust the current region accordingly, and only then  proceed  with  the
       actual import. is designed for processing massive point cloud datasets, for example raw LIDAR or
       sidescan sonar swath data. It has been tested with datasets as large as tens of billion of
       points (705GB in a single file).

       Available statistics for populating the raster are (method):

       n                                                            number of points in cell

       min                                                          minimum value of points in cell

       max                                                          maximum value of points in cell

       range                                                        range of points in cell

       sum                                                          sum of points in cell

       mean                                                         average value of points in cell

       stddev                                                       standard deviation of points in cell

       variance                                                     variance of points in cell

       coeff_var                                                    coefficient of variance of points in cell

       median                                                       median value of points in cell

       percentile                                                   pth percentile of points in cell

       skewness                                                     skewness of points in cell

       trimmean                                                     trimmed mean of points in cell

           ·   Variance and derivatives use the biased estimator (n). [subject to change]

           ·   Coefficient of variance is given in percentage and defined as (stddev/mean)*100.

       It  is  also  possible  to  bin  and  store  another  data column (e.g. backscatter) while
       simultaneously filtering and scaling both the data column values and the z range.


   Gridded data
       If data is known to be on a regular grid can reconstruct  the  map  perfectly  as
       long  as  some care is taken to set up the region correctly and that the data’s native map
       projection is used. A typical method would involve determining the grid resolution  either
       by  examining  the data’s associated documentation or by studying the text file. Next scan
       the data with’s -s (or -g) flag to find the input data’s bounds. GRASS  uses  the
       cell-center  raster  convention  where  data  points  fall within the center of a cell, as
       opposed to the grid-node convention. Therefore you will need to grow  the  region  out  by
       half  a  cell  in  all directions beyond what the scan found in the file. After the region
       bounds and resolution are set correctly with g.region, run using the n method and
       verify  that n=1 at all places.  r.univar can help. Once you are confident that the region
       exactly matches the data proceed to run using one  of  the  mean,  min,  max,  or
       median methods. With n=1 throughout, the result should be identical regardless of which of
       those methods are used.

   Memory use
       While the input file can be arbitrarily large, will use a large amount of  system
       memory for large raster regions (10000x10000).  If the module refuses to start complaining
       that there isn’t enough memory, use the percent parameter to run  the  module  in  several
       passes.   In  addition  using a less precise map format (CELL [integer] or FCELL [floating
       point]) will use less memory than a DCELL [double precision floating  point]  output  map.
       Methods  such  as  n, min, max, sum will also use less memory, while stddev, variance, and
       coeff_var will use more.  The aggregate functions median, percentile, skewness and trimmed
       mean  will  use even more memory and may not be appropriate for use with arbitrarily large
       input files.

       The default map type=FCELL is intended as compromise between preserving data precision and
       limiting  system  resource  consumption.  If reading data from a stdin stream, the program
       can only run using a single pass.

   Setting region bounds and resolution
       You can use the -s scan flag to find the extent of the input data (and thus point density)
       before  performing the full import. Use g.region to adjust the region bounds to match. The
       -g shell style flag prints the extent suitable as parameters  for  g.region.   A  suitable
       resolution can be found by dividing the number of input points by the area covered. e.g.
       wc -l inputfile.txt
       g.region -p
       # points_per_cell = n_points / (rows * cols)
       g.region -e
       # UTM location:
       # points_per_sq_m = n_points / (ns_extent * ew_extent)
       # Lat/Lon location:
       # points_per_sq_m = n_points / (ns_extent * ew_extent*cos(lat) * (1852*60)^2)

       If  you  only  intend to interpolate the data with and, then there is
       little point to setting the region resolution so fine that you only catch one  data  point
       per cell -- you might as well use " -zbt" directly.

       Points  falling  outside  the current region will be skipped. This includes points falling
       exactly on the southern region bound.  (to capture those adjust the region with  "g.region
       s=s-0.000001"; see g.region)

       Blank lines and comment lines starting with the hash symbol (#) will be skipped.

       The  zrange parameter may be used for filtering the input data by vertical extent. Example
       uses might include preparing multiple raster sections to be  combined  into  a  3D  raster
       array with, or for filtering outliers on relatively flat terrain.

       In  varied  terrain  the  user may find that min maps make for a good noise filter as most
       LIDAR noise is from premature hits. The min map may also be useful to find the  underlying
       topography in a forested or urban environment if the cells are over sampled.

       The  user can use a combination of output maps to create custom filters. e.g. use
       r.mapcalc to create a mean-(2*stddev) map. [In this example the user may want to include a
       lower  bound  filter  in  r.mapcalc  to  remove  highly  variable  points (small n) or run
       r.neighbors to smooth the stddev map before further use.]

   Alternate value column
       The value_column parameter can be used in specialized cases when you  want  to  filter  by
       z-range  but  bin  and  store  another column’s data. For example if you wanted to look at
       backscatter values between 1000 and 1500 meters elevation.  This  is  particularly  useful
       when  using  to prepare depth slices for a 3D raster — the zrange option defines
       the depth slice but the data values stored in the voxels describe an additional dimension.
       As with the z column, a filtering range and scaling factor may be applied.

       If  the raster map is to be reprojected, it may be more appropriate to reproject the input
       points with m.proj or cs2cs before running

   Interpolation into a DEM
       The vector engine’s topographic abilities introduce a finite memory  overhead  per  vector
       point  which will typically limit a vector map to approximately 3 million points (~ 1750^2
       cells). If you want more, use the -b flag to  skip  building  topology.  Without
       topology, however, all you’ll be able to do with the vector map is display with d.vect and
       interpolate with  Run r.univar on your raster  map  to  check  the  number  of
       non-NULL cells and adjust bounds and/or resolution as needed before proceeding.

       Typical commands to create a DEM using a regularized spline fit:
       r.univar lidar_min -z type=point in=lidar_min out=lidar_min_pt in=lidar_min_pt elev=lidar_min.rst

   Import of x,y,string data  is  expecting numeric values as z column. In order to perform a occurrence count
       operation even on x,y data with non-numeric attribute(s), the data can be  imported  using
       either  the  x or y coordinate as a fake z column for method=n (count number of points per
       grid cell), the z values are ignored anyway.


   Import of x,y,z ASCII into DEM
       Sometimes elevation data are delivered as x,y,z ASCII files instead of  a  raster  matrix.
       The  import  procedure  consists of a few steps: calculation of the map extent, setting of
       the computational region accordingly with an additional extension into all  directions  by
       half a raster cell in order to register the elevation points at raster cell centers.

       Note:  if  the z column is separated by several spaces from the coordinate columns, it may
       be sufficient to adapt the z position value.
       # Important: observe the raster spacing from the ASCII file:
       # ASCII file format (example):
       # 630007.5 228492.5 141.99614
       # 630022.5 228492.5 141.37904
       # 630037.5 228492.5 142.29822
       # 630052.5 228492.5 143.97987
       # ...
       # In this example the distance is 15m in x and y direction.
       # detect extent, print result as g.region parameters separator=space -s -g
       # ... n=228492.5 s=215007.5 e=644992.5 w=630007.5 b=55.578793 t=156.32986
       # set computational region, along with the actual raster resolution
       # as defined by the point spacing in the ASCII file:
       g.region n=228492.5 s=215007.5 e=644992.5 w=630007.5 res=15 -p
       # now enlarge computational region by half a raster cell (here 7.5m) to
       # store the points as cell centers:
       g.region n=n+7.5 s=s-7.5 w=w-7.5 e=e+7.5 -p
       # import XYZ ASCII file, with z values as raster cell values separator=space method=mean output=myelev
       # univariate statistics for verification of raster values
       r.univar myelev

   Import of LiDAR data and DEM creation
       Import the Jockey’s Ridge, NC, LIDAR dataset  (compressed  file  "lidaratm2.txt.gz"),  and
       process it into a clean DEM:
       # scan and set region bounds -s -g separator="," in=lidaratm2.txt
       g.region n=35.969493 s=35.949693 e=-75.620999 w=-75.639999
       g.region res=0:00:00.075 -a
       # create "n" map containing count of points per cell for checking density in=lidaratm2.txt out=lidar_n separator="," method=n zrange=-2,50
       # check point density [rho = n_sum / (rows*cols)]
       r.univar lidar_n
       # create "min" map (elevation filtered for premature hits) in=lidaratm2.txt out=lidar_min separator="," method=min zrange=-2,50
       # set computational region to area of interest
       g.region n=35:57:56.25N s=35:57:13.575N w=75:38:23.7W e=75:37:15.675W
       # check number of non-null cells (try and keep under a few million)
       r.univar lidar_min
       # convert to points -z type=point in=lidar_min out=lidar_min_pt
       # interpolate using a regularized spline fit in=lidar_min_pt elev=lidar_min.rst
       # set color scale to something interesting
       r.colors lidar_min.rst rule=bcyr -n -e
       # prepare a 1:1:1 scaled version for NVIZ visualization (for lat/lon input)
       r.mapcalc "lidar_min.rst_scaled = lidar_min.rst / (1852*60)"
       r.colors lidar_min.rst_scaled rule=bcyr -n -e


           ·   Support for multiple map output from a single run.
               method=string[,string,...] output=name[,name,...]
               This can be easily handled by a wrapper script, with the added benefit of it being
               very simple to parallelize that way.


           ·   "nan" can leak into coeff_var maps.
               Cause unknown. Possible work-around: "r.null setnull=nan"
       If you encounter any problems (or solutions!) please contact the GRASS Development Team.


        g.region, m.proj, r.fillnulls,,,, r.mapcalc, r.neighbors,,,, r.univar,,

        v.lidar.correction, v.lidar.edgedetection, v.lidar.growing, v.outlier,

       pv - The UNIX pipe viewer utility

       Overview: Interpolation and Resampling in GRASS GIS


       Hamish Bowman, Department of Marine Science, University of Otagom New Zealand
       Extended  by  Volker  Wichmann  to  support  the  aggregate  functions median, percentile,
       skewness and trimmed mean.


       Available at: source code (history)

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       © 2003-2019 GRASS Development Team, GRASS GIS 7.8.2 Reference Manual