r.random.walk - GRASS GIS manual

NAME

r.random.walk - Performs a 2D random walk inside the computational region and returns the resulting walk.

KEYWORDS

SYNOPSIS

r.random.walk

r.random.walk --help

r.random.walk [-ast] output=name [steps=integer] [directions=string] [memory=memory in MB] [seed=integer] [nprocs=integer] [nwalkers=integer] [--overwrite] [--help] [--verbose] [--quiet] [--ui]

Flags:

-a: Perform a self-avoiding random walk
-s: Generate random seed (result is non-deterministic).
-t: Each walker starts from the same point.
--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:

output=name [required]: Name for output raster map
steps=integer: How many steps to take during walk.; Default: 100000
directions=string: How many directions should be used during walk.; Options: 4, 8; Default: 4
memory=memory in MB: Maximum memory to be used (in MB); Cache size for raster rows; Default: 300
seed=integer: Seed for random number generator
nprocs=integer: Number of threads for parallel computing; Default: 1
nwalkers=integer: Number of walkers.; Default: 1

DESCRIPTION
EXAMPLE
SEE ALSO
AUTHOR

DESCRIPTION

The r.random.walk module generates a 2D random walk across the current computational region. The module provides control of the number of steps and directions (4 or 8) a walker can take and allows the walker's behavior to be set to be self-avoiding (Madras et al., 1996) or allow revisits. The output displays the frequency the walker visited each cell or the average frequency. The module can run multiple walks in parallel. It either samples the same starting location for each walk or generates a unique starting position for each walker.

EXAMPLE

Using the North Carolina full sample dataset:

# set computational region
g.region raster=elevation -p

# calculate smoothed random walk from a single starting locations.
r.random.walk -at output=random_walk_smooth_paths directions=8 steps=100000 memory=1800 seed=1 nprocs=6 nwalkers=100

Smoothed random walk (Single Starting Location)

# calculate smoothed random walk from a multiple starting locations.
r.random.walk -as output=random_walk_smooth directions=8 steps=100000 memory=1800 nprocs=6 nwalkers=100