- Script for creating demand table which determines the quantity of land change expected.
r.futures.demand development=name[,name,...] subregions=name observed_population=name projected_population=name simulation_times=integer[,integer,...] method=string[,string,...] [plot=name] demand=name [separator=character] [--overwrite] [--help] [--verbose] [--quiet] [--ui]
- Allow output files to overwrite existing files
- Print usage summary
- Verbose module output
- Quiet module output
- 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)
- Save plotted relationship between developed cells and population into a file
- File type is given by extension (.pdf, .png, .svg)
- demand=name [required]
- Output CSV file with demand (times as rows, regions as columns)
- Separator used in CSV files
- Special characters: pipe, comma, space, tab, newline
- Default: comma
module of FUTURES
determines the quantity of expected land changed.
It creates a demand table as the number of cells to be converted
at each time step for each subregion based on the relation
between the population and developed land in the past years.
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.
The same table is needed for projected population
The categories of the input raster subregions must
match the identifiers of subregions in files given in observed_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
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)])
seq -s, 2015 2030
The format of the output demand table is:
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 separator
In case the demand values would be negative (in case of population decrease
or if the relation is inversely proportional) the values are turned into zeros,
since FUTURES does not simulate change from developed to undeveloped sites.
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.
The file format 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
computes the relation
between population and developed area using simple
regression and in case of method exp_approach
It is possible to manually create a custom demand file where each column could be taken from
a run with most suitable method.
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.
- 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.
- 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.
Anna Petrasova, NCSU GeoForAll
r.futures.demand source code
Latest change: Thursday Feb 03 09:32:35 2022 in commit: f17c792f5de56c64ecfbe63ec315307872cf9d5c
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