Note: This document is for an older version of GRASS GIS that will be discontinued soon. You should upgrade, and read the current manual page.
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 [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
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.
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,...
...
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.
FUTURES,
r.futures.pga,
r.futures.devpressure,
r.futures.potsurface,
r.futures.demand,
r.futures.calib,
r.sample.category
-
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
Anna Petrasova,
NCSU GeoForAllSOURCE CODE
Available at:
r.futures.potential source code
(history)
Latest change: Wednesday Mar 16 11:48:02 2022 in commit: 240e15b24997e1aa5cd10e62682178f4d2bf63d7
Note: This document is for an older version of GRASS GIS that will be discontinued soon. You should upgrade, and read the current manual page.
Main index |
Raster index |
Topics index |
Keywords index |
Graphical index |
Full index
© 2003-2023
GRASS Development Team,
GRASS GIS 8.2.2dev Reference Manual