r.futures.potential - GRASS manual

NAME

r.futures.potential - Module for computing development potential as input to r.futures.pga

KEYWORDS

SYNOPSIS

r.futures.potential

r.futures.potential --help

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] [--help] [--verbose] [--quiet] [--ui]

Flags:

-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
--ui: Force launching GUI dialog

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; Options: 1-20; Default: 1
max_variables=integer: Maximum number of predictors considered; Options: 1-20
nprocs=integer [required]: Number of parallel processes for dredging; Options: 1-50; Default: 1
dredge_output=name: Output CSV file summarizing all models

DESCRIPTION
- Format
NOTES
REFERENCES
SEE ALSO
AUTHORS

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.

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
Sanchez, G.M., A. Petrasova, A., M.M. Skrip, E.L. Collins, M.A. Lawrimore, J.B. Vogler, A. Terando, J. Vukomanovic, H. Mitasova, and R.K. Meentemeyer (2023). Spatially interactive modeling of land change identifies location-specific adaptations most likely to lower future flood risk. Sci Rep 13, 18869. DOI: 10.1038/s41598-023-46195-9

AUTHORS

Corresponding author:
Anna Petrasova, akratoc ncsu edu, Center for Geospatial Analytics, NCSU

Original standalone version:
Ross K. Meentemeyer,
Wenwu Tang,
Monica A. Dorning,
John B. Vogler,
Nik J. Cunniffe,
Douglas A. Shoemaker
(Department of Geography and Earth Sciences, UNC Charlotte)
Jennifer A. Koch (Center for Geospatial Analytics, NCSU)

Port to GRASS and GRASS-specific additions:
Vaclav Petras, NCSU GeoForAll

Development pressure, demand, calibration, validation, preprocessing tools and maintenance:
Anna Petrasova, NCSU GeoForAll

Climate forcing submodel:
Anna Petrasova, NCSU GeoForAll
Georgina Sanchez, Center for Geospatial Analytics, NCSU

Zoning:
Margaret Lawrimore, Center for Geospatial Analytics, NCSU
Anna Petrasova, NCSU GeoForAll

SOURCE CODE

Available at: r.futures.potential source code (history)

Accessed: Monday Jul 27 12:29:18 2026