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NAME

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

KEYWORDS

raster, statistics

SYNOPSIS

r.futures.potential
r.futures.potential --help
r.futures.potential [-d] input=name output=name columns=name[,name,...] developed_column=name subregions_column=name [min_variables=integer] [max_variables=integer] [--overwrite] [--help] [--verbose] [--quiet] [--ui]

Flags:

-d
Use dredge fuction 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
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
min_variables=integer
Minimum number of predictors considered
Options: 1-20
Default: 1
max_variables=integer
Maximum number of predictors considered
Options: 1-20

Table of contents

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 (but with tabs as delimiters). 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

AUTHOR

Anna Petrasova, NCSU GeoForAll

Last changed: $Date: 2018-09-14 21:49:22 -0400 (Fri, 14 Sep 2018) $

SOURCE CODE

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

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