NAME
r.futures.potential - Module for computing development potential as input to r.futures.pgaKEYWORDS
raster, statisticsSYNOPSIS
r.futures.potential
r.futures.potential --helpr.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.categoryREFERENCES
- 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.
- 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.
AUTHOR
Anna Petrasova, NCSU OSGeoRELLast changed: $Date: 2017-07-27 05:15:52 +0200 (Thu, 27 Jul 2017) $
SOURCE CODE
Available at: r.futures.potential source code (history)
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