r.futures.potsurface - GRASS manual

NAME

r.futures.potsurface - Module for computing development potential surface from CSV file created by r.futures.potential and predictors

KEYWORDS

raster, statistics

SYNOPSIS

r.futures.potsurface

r.futures.potsurface --help

r.futures.potsurface input=name subregions=name output=name [separator=character] [--overwrite] [--help] [--verbose] [--quiet] [--ui]

Flags:

--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]: CSV file with coefficients
subregions=name [required]: Raster map of subregions
output=name [required]: Output probability raster
separator=character: Separator used in input CSV file; Special characters: pipe, comma, space, tab, newline; Default: comma

DESCRIPTION
EXAMPLES
REFERENCES
SEE ALSO
AUTHORS

DESCRIPTION

Module r.futures.potsurface is a support tool for computing development probability surface based on maps and coefficients selected by r.futures.potential. It computes the initial probability surface used in the patch growing algorithm in r.futures.simulation. It is not necessary to use this module, however it is useful to inspect the potential surface to better understand the input data and how the predictors influence the probability. The values range from 0 (unlikely to be developed) to 1 (high probability of development).

The inputs are the output file from r.futures.potential and the name of the subregions raster map.

EXAMPLES

r.futures.potsurface input=potential.csv subregions=counties output=pot_surface

Illustration of a potential
surface in 3D, with a raster of the developed cells (in red) and
undeveloped cells (in green) draped onto the surface.

Figure: We can visualize the potential surface in 3D and drape raster representing developed (red) and undeveloped (green) cells over it.

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.potsurface source code (history)

Accessed: Monday Jul 27 12:29:18 2026