Note: This addon document is for an older version of GRASS GIS that will be discontinued soon. You should upgrade your GRASS GIS installation, and read the current addon manual page.
NAME
r.futures.potential - Module for computing development potential as input to r.futures.pgaKEYWORDS
raster, statisticsSYNOPSIS
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
- 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
- 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
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 (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.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. 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 GeoForAllLast changed: $Date: 2018-09-14 21:49:22 -0400 (Fri, 14 Sep 2018) $
SOURCE CODE
Available at: r.futures.potential source code (history)
Latest change: Mon Nov 1 12:13:58 2021 in commit: 1a9c817b17fa46763fab60da21db214ed9ff6cb1
Note: This addon document is for an older version of GRASS GIS that will be discontinued soon. You should upgrade your GRASS GIS installation, and read the current addon manual page.
Main index | Raster index | Topics index | Keywords index | Graphical index | Full index
© 2003-2023 GRASS Development Team, GRASS GIS 7.8.8dev Reference Manual