Provided by: grass-doc_7.0.3-1build1_all 
NAME
r.in.xyz - Creates a raster map from an assemblage of many coordinates using univariate statistics.
KEYWORDS
raster, import, conversion, aggregation, ASCII, LIDAR
SYNOPSIS
r.in.xyz
r.in.xyz --help
r.in.xyz [-sgi] input=name output=name [method=string] [type=string] [separator=character]
[x=integer] [y=integer] [z=integer] [skip=integer] [zrange=min,max] [zscale=float]
[value_column=integer] [vrange=min,max] [vscale=float] [percent=integer] [pth=integer]
[trim=float] [--overwrite] [--help] [--verbose] [--quiet] [--ui]
Flags:
-s
Scan data file for extent then exit
-g
In scan mode, print using shell script style
-i
Ignore broken lines
--overwrite
Allow output files to overwrite existing files
--help
Print usage summary
--verbose
Verbose module output
--quiet
Quiet module output
--ui
Force launching GUI dialog
Parameters:
input=name [required]
ASCII file containing input data (or "-" to read from stdin)
output=name [required]
Name for output raster map
method=string
Statistic to use for raster values
Options: n, min, max, range, sum, mean, stddev, variance, coeff_var, median, percentile, skewness,
trimmean
Default: mean
type=string
Storage type for resultant raster map
Options: CELL, FCELL, DCELL
Default: FCELL
separator=character
Field separator
Special characters: pipe, comma, space, tab, newline
Default: pipe
x=integer
Column number of x coordinates in input file (first column is 1)
Default: 1
y=integer
Column number of y coordinates in input file
Default: 2
z=integer
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
skip=integer
Number of header lines to skip at top of input file
Default: 0
zrange=min,max
Filter range for z data (min,max)
zscale=float
Scale to apply to z data
Default: 1.0
value_column=integer
Alternate column number of data values in input file
If not given (or set to 0) the z-column data is used
Default: 0
vrange=min,max
Filter range for alternate value column data (min,max)
vscale=float
Scale to apply to alternate value column data
Default: 1.0
percent=integer
Percent of map to keep in memory
Options: 1-100
Default: 100
pth=integer
pth percentile of the values
Options: 1-100
trim=float
Discard <trim> percent of the smallest and <trim> percent of the largest observations
Options: 0-50
DESCRIPTION
The r.in.xyz 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.
r.in.xyz 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:
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.
NOTES
Gridded data
If data is known to be on a regular grid r.in.xyz 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 r.in.xyz’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 r.in.xyz 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 r.in.xyz 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, r.in.xyz 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 r.to.vect and v.surf.rst, 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 "v.in.ascii -zbt" directly.
Filtering
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 r.to.rast3, 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 r.in.xyz 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 r.in.xyz 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.
Reprojection
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 r.in.xyz.
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 r.to.vect -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 v.surf.rst. 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
r.to.vect -z type=point in=lidar_min out=lidar_min_pt
v.surf.rst in=lidar_min_pt elev=lidar_min.rst
Import of x,y,string data
r.in.xyz 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.
EXAMPLE
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
r.in.xyz -s separator="," in=lidaratm2.txt out=test
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
r.in.xyz in=lidaratm2.txt out=lidar_n separator="," method=n zrange=-2,50
# check point density [rho = n_sum / (rows*cols)]
r.univar lidar_n | grep sum
# create "min" map (elevation filtered for premature hits)
r.in.xyz 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 | grep ’^n:’
# convert to points
r.to.vect -z type=point in=lidar_min out=lidar_min_pt
# interpolate using a regularized spline fit
v.surf.rst 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
TODO
• 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.
• Add two new flags for support for direct binary input from libLAS for LIDAR data and MB-System’s
mbio for multi-beam bathymetry data.
note: See the new r.in.lidar module for this.
KNOWN ISSUES
• n map sum can be ever-so-slightly more than `wc -l` with e.g. percent=10 or less.
Cause unknown.
• n map percent=100 and percent=xx maps differ slightly (point will fall above/below the
segmentation line)
Investigate with "r.mapcalc diff = bin_n.100 - bin_n.33" etc.
Cause unknown.
• "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.
SEE ALSO
g.region, m.proj, r.fillnulls, r.in.ascii, r.in.lidar, r3.in.xyz, r.mapcalc, r.neighbors, r.out.xyz,
r.to.rast3, r.to.vect, r.univar, v.in.ascii, v.surf.rst
v.lidar.correction, v.lidar.edgedetection, v.lidar.growing, v.outlier, v.surf.bspline
pv - The UNIX pipe viewer utility
AUTHORS
Hamish Bowman
Department of Marine Science
University of Otago
New Zealand
Extended by Volker Wichmann to support the aggregate functions median, percentile, skewness and trimmed
mean.
Last changed: $Date: 2015-11-20 12:34:12 +0100 (Fri, 20 Nov 2015) $
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