r.futures.potential
Module for computing development potential as input to r.futures.pga
r.futures.potential [-d] input=name output=name [separator=character] columns=name [,name,...] developed_column=name subregions_column=name [random_column=string] [fixed_columns=string [,string,...]] [min_variables=integer] [max_variables=integer] nprocs=integer [dredge_output=name] [--overwrite] [--verbose] [--quiet] [--qq] [--ui]
Example:
r.futures.potential input=name output=name columns=name developed_column=name subregions_column=name nprocs=1
grass.script.run_command("r.futures.potential", input, output, separator="comma", columns, developed_column, subregions_column, random_column=None, fixed_columns=None, min_variables=1, max_variables=None, nprocs=1, dredge_output=None, flags=None, overwrite=False, verbose=False, quiet=False, superquiet=False)
Example:
gs.run_command("r.futures.potential", input="name", output="name", columns="name", developed_column="name", subregions_column="name", nprocs=1)
Parameters
input=name [required]
Name of input vector map
Or data source for direct OGR access
output=name [required]
Output Potential file
separator=character
Separator used in output file
Special characters: pipe, comma, space, tab, newline
Default: comma
columns=name [,name,...] [required]
Names of attribute columns representing sampled predictors
developed_column=name [required]
Name of attribute column representing development
subregions_column=name [required]
Name of attribute column representing subregions
random_column=string
Random effect predictor
fixed_columns=string [,string,...]
Predictor columns that will be used for all models when dredging
min_variables=integer
Minimum number of predictors considered
Allowed values: 1-20
Default: 1
max_variables=integer
Maximum number of predictors considered
Allowed values: 1-20
nprocs=integer [required]
Number of parallel processes for dredging
Allowed values: 1-50
Default: 1
dredge_output=name
Output CSV file summarizing all models
-d
Use dredge function to find best model
--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 vector map
Or data source for direct OGR access
Used as: input, vector, name
output : str, required
Output Potential file
Used as: output, file, name
separator : str, optional
Separator used in output file
Special characters: pipe, comma, space, tab, newline
Used as: input, separator, character
Default: comma
columns : str | list[str], required
Names of attribute columns representing sampled predictors
Used as: input, dbcolumn, name
developed_column : str, required
Name of attribute column representing development
Used as: input, dbcolumn, name
subregions_column : str, required
Name of attribute column representing subregions
Used as: input, dbcolumn, name
random_column : str, optional
Random effect predictor
fixed_columns : str | list[str], optional
Predictor columns that will be used for all models when dredging
min_variables : int, optional
Minimum number of predictors considered
Allowed values: 1-20
Default: 1
max_variables : int, optional
Maximum number of predictors considered
Allowed values: 1-20
nprocs : int, required
Number of parallel processes for dredging
Allowed values: 1-50
Default: 1
dredge_output : str, optional
Output CSV file summarizing all models
Used as: output, file, name
flags : str, optional
Allowed values: d
d
Use dredge function to find best model
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
Module r.futures.potential implements POTENTIAL submodel as a part of
FUTURES land change model. POTENTIAL is implemented
using a set of coefficients that relate a selection of site suitability
factors to the probability of a place becoming developed. This is
implemented using the parameter table in combination with maps of those
site suitability factors (mapped predictors). The coefficients are
obtained by conducting multilevel logistic regression in R with package
lme4 where
the coefficients may vary by subregions. The best model is selected
automatically using dredge
function from package
MuMIn (which
has numerous caveats).
Module r.futures.potential can run it two modes. Without the -d flag, it uses all the given predictors to construct the model. With -d flag, it evaluates all the different combinations of predictors and picks the best one based on AIC.
Format
The format of the output file is a CSV file (use option separator to change default separator comma). The header contains the names of the predictor maps and the first column contains the identifiers of the subregions. The order of columns is important, the second column represents intercept, the third development pressure and then the predictors. Therefore the development pressure column must be specified as the first column in option columns.
ID,Intercept,devpressure_0_5,slope,road_dens_perc,forest_smooth_perc,...
37037,-1.873,12.595,-0.0758,0.0907,-0.0223,...
37063,-2.039,12.595,-0.0758,0.0907,-0.0223,...
37069,-1.795,12.595,-0.0758,0.0907,-0.0223,...
37077,-1.264,12.595,-0.0758,0.0907,-0.0223,...
37085,-1.925,12.595,-0.0758,0.0907,-0.0223,...
...
NOTES
Note that this module is designed to automate the FUTURES workflow by brute-force selection of model, which has numerous caveats.
In case there is only one subregion, R function glm is used instead of glmer.
EXAMPLES
SEE ALSO
FUTURES, r.futures.pga, r.futures.devpressure, r.futures.potsurface, r.futures.demand, r.futures.calib, r.sample.category
REFERENCES
- Meentemeyer, R. K., Tang, W., Dorning, M. A., Vogler, J. B., Cunniffe, N. J., & Shoemaker, D. A. (2013). FUTURES: Multilevel Simulations of Emerging Urban-Rural Landscape Structure Using a Stochastic Patch-Growing Algorithm. Annals of the Association of American Geographers, 103(4), 785-807. DOI: 10.1080/00045608.2012.707591
- Dorning, M. A., Koch, J., Shoemaker, D. A., & Meentemeyer, R. K. (2015). Simulating urbanization scenarios reveals tradeoffs between conservation planning strategies. Landscape and Urban Planning, 136, 28-39. DOI: 10.1016/j.landurbplan.2014.11.011
- Petrasova, A., Petras, V., Van Berkel, D., Harmon, B. A., Mitasova, H., & Meentemeyer, R. K. (2016). Open Source Approach to Urban Growth Simulation. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 953-959. DOI: 10.5194/isprsarchives-XLI-B7-953-2016
AUTHOR
Anna Petrasova, NCSU GeoForAll
SOURCE CODE
Available at: r.futures.potential source code
(history)
Latest change: Thursday Mar 20 21:36:57 2025 in commit 7286ecf