r.pi.searchtime.pr
Individual-based dispersal model for connectivity analysis (time-based) using iterative removal of patches
r.pi.searchtime.pr [-ac] input=name [suitability=string] output=name [out_immi=string] keyval=integer step_length=integer [perception=integer] [multiplicator=float] n=integer percent=float stats=string [,string,...] dif_stats=string [,string,...] [maxsteps=integer] [out_freq=integer] [title="phrase"] [--overwrite] [--verbose] [--quiet] [--qq] [--ui]
Example:
r.pi.searchtime.pr input=name output=name keyval=integer step_length=integer n=integer percent=float stats=string dif_stats=string
grass.script.run_command("r.pi.searchtime.pr", input, suitability=None, output, out_immi=None, keyval, step_length, perception=None, multiplicator=None, n, percent, stats, dif_stats, maxsteps=None, out_freq=None, title=None, flags=None, overwrite=False, verbose=False, quiet=False, superquiet=False)
Example:
gs.run_command("r.pi.searchtime.pr", input="name", output="name", keyval=integer, step_length=integer, n=integer, percent=float, stats="string", dif_stats="string")
Parameters
input=name [required]
Name of input raster map
suitability=string
Name of the costmap with values from 0-100
output=name [required]
Name for output raster map
out_immi=string
Name of the optional raster file for patch immigrants count
keyval=integer [required]
Category value of the patches
step_length=integer [required]
Length of a single step measured in pixels
perception=integer
Perception range
multiplicator=float
Attractivity of patches [1-inf]
n=integer [required]
Number of individuals
percent=float [required]
Percentage of individuals which must have arrived successfully to stop the model-run
stats=string [,string,...] [required]
Statistical method to perform on the pathlengths of the individuals
Allowed values: average, variance, standard deviation, median, min, max
dif_stats=string [,string,...] [required]
Statistical method to perform on the difference values
Allowed values: average, variance, standard deviation, median, min, max
maxsteps=integer
Maximum steps for each individual
out_freq=integer
Output an intermediate state of simulation each [out_freq] steps
title="phrase"
Title for resultant raster map
-a
Set for 8 cell-neighbors. 4 cell-neighbors are default
-c
Include cost of the path in the calculation of steps
--overwrite
Allow output files to overwrite existing files
--help
Print usage summary
--verbose
Verbose module output
--quiet
Quiet module output
--qq
Very quiet module output
--ui
Force launching GUI dialog
input : str, required
Name of input raster map
Used as: input, raster, name
suitability : str, optional
Name of the costmap with values from 0-100
Used as: input, raster
output : str, required
Name for output raster map
Used as: output, raster, name
out_immi : str, optional
Name of the optional raster file for patch immigrants count
Used as: output, raster
keyval : int, required
Category value of the patches
step_length : int, required
Length of a single step measured in pixels
perception : int, optional
Perception range
multiplicator : float, optional
Attractivity of patches [1-inf]
n : int, required
Number of individuals
percent : float, required
Percentage of individuals which must have arrived successfully to stop the model-run
stats : str | list[str], required
Statistical method to perform on the pathlengths of the individuals
Allowed values: average, variance, standard deviation, median, min, max
dif_stats : str | list[str], required
Statistical method to perform on the difference values
Allowed values: average, variance, standard deviation, median, min, max
maxsteps : int, optional
Maximum steps for each individual
out_freq : int, optional
Output an intermediate state of simulation each [out_freq] steps
title : str, optional
Title for resultant raster map
Used as: "phrase"
flags : str, optional
Allowed values: a, c
a
Set for 8 cell-neighbors. 4 cell-neighbors are default
c
Include cost of the path in the calculation of steps
overwrite: bool, optional
Allow output files to overwrite existing files
Default: False
verbose: bool, optional
Verbose module output
Default: False
quiet: bool, optional
Quiet module output
Default: False
superquiet: bool, optional
Very quiet module output
Default: False
DESCRIPTION
Analysis of patch relevance to maintain the landscape connectivity using individual-based dispersal model for connectivity analysis (time-based).
This modules provides information about the importance of single patches for maintaining the connectivity of individual fragments derived of a landcover classification. Unlike r.pi.energy.pr this module provides information about the differences in time from emigration to immigration. The individual based dispersal model results are based on the step length and range, the perception distance and the attractivity to move towards patches.
NOTES
The suitability matrix impacts the step direction of individuals. If individuals are moving beyond the mapset borders the indivuals are set back to their original source patches.
EXAMPLE
An example for the North Carolina sample dataset:
The patch relevance concerning connectivity are based on patches of the landclass96 raster class 5 amd the time (amount of steps) from emigration to immigration is computed. The step length is set to 5 pixel, the output statistics are set to average time and variance of searchtime. For each patch 1000 individuals were released and the model stopped when at least 80% of all individuals sucessfully immigrated:
r.pi.searchtime.pr input=landclass96 output=searchtime_iter1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 dif_stats=average
setting the perception range to 10 pixel:
r.pi.searchtime.pr input=landclass96 output=searchtime_iter1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 dif_stats=average perception=10
increasing the attraction to move towards patches to 10:
r.pi.searchtime.pr input=landclass96 output=searchtime_iter1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 dif_stats=average multiplicator=10
output of each movement location for a defined step frequency. Here every 10th step is provided as output raster:
r.pi.searchtime.pr input=landclass96 output=searchtime_iter1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 dif_stats=average out_freq=10
output of a raster which immigration counts:
r.pi.searchtime.pr input=landclass96 output=searchtime1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 dif_stats=average out_immi=immi_counts
the previous examples assumed a homogeneous matrix, a heterogenous matrix can be included using a raster file which values are taken as costs for movement (0-100):
# it is assumed that our species is a forest species and cannot move
# through water, hence a cost of 100, does not like urban areas
# (class: 6, cost: 10) but can disperse through shrubland (class 4,
# cost=1) better than through grassland (class 3, cost: 2):
r.mapcalc "suit_raster = if(landclass96==5,1,if(landclass96 == 1, 10, if (landclass96==3,2, if(landclass96==4,1,if(landclass96==6,100)))))"
r.pi.searchtime.pr input=landclass96 output=searchtime1 keyval=5 step_length=5 stats=average,variance percent=80 n=1000 dif_stats=average suitability=suit_raster
SEE ALSO
r.pi.searchtime, r.pi.searchtime.mw, r.pi
AUTHORS
Programming: Elshad Shirinov
Scientific concept: Dr. Martin Wegmann
Department of Remote Sensing
Remote Sensing and Biodiversity Unit
University of Wuerzburg, Germany
Port to GRASS GIS 7: Markus Metz
SOURCE CODE
Available at: r.pi.searchtime.pr source code
(history)
Latest change: Thursday Feb 20 13:02:26 2025 in commit 53de819