**--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

**development**=*name[,**name*,...]**[required]**- Names of input binary raster maps representing development
**subregions**=*name***[required]**- Raster map of subregions
**observed_population**=*name***[required]**- CSV file with observed population in subregions at certain times
**projected_population**=*name***[required]**- CSV file with projected population in subregions at certain times
**simulation_times**=*integer[,**integer*,...]**[required]**- For which times demand is projected
**method**=*string[,**string*,...]**[required]**- Relationship between developed cells (dependent) and population (explanatory)
- Options:
*linear, logarithmic, exponential, exp_approach, logarithmic2* - Default:
*linear,logarithmic* **linear**: y = A + Bx**logarithmic**: y = A + Bln(x)**exponential**: y = Ae^(BX)**exp_approach**: y = (1 - e^(-A(x - B))) + C (SciPy)**logarithmic2**: y = A + B * ln(x - C) (SciPy)**plot**=*name*- Save plotted relationship between developed cells and population into a file
- File type is given by extension (.pfd, .png, .svg)
**demand**=*name***[required]**- Output CSV file with demand (times as rows, regions as columns)
**separator**=*character*- Separator used in CSV files
- Special characters: pipe, comma, space, tab, newline
- Default:
*comma*

The input accepts multiple (at least 2)
rasters of developed (category 1) and undeveloped areas (category 0)
from different years, ordered by time.
For these years, user has to provide the population numbers for each subregion
in parameter **observed_population** as a CSV file.
The format is as follows. First column is time
(matching the time of rasters used in parameter **development**) and
first row is the category of the subregion. The separator can be set
with parameter **separator**.

year,37037,37063,... 1985,19860,10980,... 1995,20760,12660,... 2005,21070,13090,... 2015,22000,13940,...

The same table is needed for projected population
(parameter **projected_population**).
The categories of the input raster **subregions** must
match the identifiers of subregions in files given in **observed_population**
and **projected_population**.
Parameter **simulation_times** is a comma separated list
of times for which the demand will be computed. The first time should
be the time of the developed/undeveloped raster used
in *r.futures.pga*
as a starting point for simulation. There is an easy way to create
such list using Python:

','.join([str(i) for i in range(2015, 2031)])or Bash:

seq -s, 2015 2030

The format of the output **demand** table is:

year,37037,37063,37069,... 2012,1362,6677,513,... 2013,1856,4850,1589,... 2014,1791,5972,903,... 2015,1743,5497,1094,... 2016,1722,5388,1022,... 2017,1690,5285,1077,... 2018,1667,5183,1029,... ...where each value represents the number of new developed cells in each step. It's a standard CSV file, so it can be opened in a text editor or a spreadsheet application if needed. The separator can be set with parameter

The **method** parameter allows to choose the type of relation
between population and developed area. The available methods
include linear, logarithmic (2 options), exponential and exponential approach relation.
If more than one method is checked, the best relation is selected
based on RMSE. Recommended methods are *logarithmic*, *logarithmic2*, *linear* and *exp_approach*.
Methods exponential approach and logarithmic2 require scipy
and at least 3 data points (raster maps of developed area).

An optional output **plot** is a plot of the relations for each subregion.
It allows to more effectively assess the relation suitable for each subregion.
Format of the file is determined from the extension and can be for example PNG, PDF, SVG.

Figure: Example of different relations between population and developed area
(generated with option **plot**). Starting from the left:
exponential, linear, logarithmic with 2 unknown variables, logarithmic with 3 unknown variables, exponential approach

r.futures.demand development=urban_1992,urban_2001,urban_2011 subregions=counties \ observed_population=population_trend.csv projected_population=population_projection.csv \ simulation_times=`seq -s, 2011 2035` plot=plot_demand.pdf demand=demand.csv

- 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

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

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

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