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Spatial patterns of land use change due to urbanization and its impact on the landscape are the subject of ongoing research. Urban growth scenario simulation is a powerful tool for exploring these impacts and empowering planners to make informed decisions. We present FUTURES (FUTure Urban – Regional Environment Simulation) – a patch-based, stochastic, multi-level land change modeling framework as a case showing how what was once a closed and inaccessible model benefited from integration with open source GIS.We will describe our motivation for releasing this project as open source and the advantages of integrating it with GRASS GIS, a free, libre and open source GIS and research platform for the geospatial domain. GRASS GIS provides efficient libraries for FUTURES model development as well as standard GIS tools and graphical user interface for model users. Releasing FUTURES as a GRASS GIS add-on simplifies the distribution of FUTURES across all main operating systems and ensures the maintainability of our project in the future. We will describe FUTURES integration into GRASS GIS and demonstrate its usage on a case study in Asheville, North Carolina. The developed dataset and tutorial for this case study enable researchers to experiment with the model, explore its potential or even modify the model for their applications.
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OPEN SOURCE APPROACH TO URBAN GROWTH SIMULATION
A. Petrasovaa,b,
, V. Petrasa,b , D. Van Berkela, B. A. Harmona,d , H. Mitasovaa,b, R. K. Meentemeyera,c
aCenter for Geospatial Analytics, North Carolina State University, USA - dbvanber@ncsu.edu
bDepartment of Marine, Earth, and Atmospheric Sciences, North Carolina State University, USA - (vpetras, akratoc, hmitaso)@ncsu.edu
cDepartment of Forestry and Environmental Resources, North Carolina State University, USA - rkmeente@ncsu.edu
dDepartment of Landscape Architecture, North Carolina State University, USA - baharmon@ncsu.edu
Commission VII, SpS10 - FOSS4G: FOSS4G Session (coorganized with OSGeo)
KEY WORDS: GRASS GIS, FUTURES, urbanization, land change, open science, simulation
ABSTRACT:
Spatial patterns of land use change due to urbanization and its impact on the landscape are the subject of ongoing research. Urban
growth scenario simulation is a powerful tool for exploring these impacts and empowering planners to make informed decisions. We
present FUTURES (FUTure Urban – Regional Environment Simulation) – a patch-based, stochastic, multi-level land change modeling
framework as a case showing how what was once a closed and inaccessible model benefited from integration with open source GIS. We
will describe our motivation for releasing this project as open source and the advantages of integrating it with GRASS GIS, a free, libre
and open source GIS and research platform for the geospatial domain. GRASS GIS provides efficient libraries for FUTURES model
development as well as standard GIS tools and graphical user interface for model users. Releasing FUTURES as a GRASS GIS add-on
simplifies the distribution of FUTURES across all main operating systems and ensures the maintainability of our project in the future.
We will describe FUTURES integration into GRASS GIS and demonstrate its usage on a case study in Asheville, North Carolina. The
developed dataset and tutorial for this case study enable researchers to experiment with the model, explore its potential or even modify
the model for their applications.
1. INTRODUCTION
Population growth in cities worldwide drives changes in land use
often negatively impacting the environments in which people live
and undermining the resilience of local ecosystems. The need
to understand the trade-offs urban planners are facing gave rise
to a number of different land change simulation models, which
proved to be powerful tools for exploring alternative scenarios
and their impacts on various aspects of human-environmental sys-
tems (Chaudhuri and Clarke, 2013, Verburg et al., 2002, Sohl
et al., 2007, Waddell, 2002). Despite the influence of the spa-
tial structure and connectivity of urbanizing landscapes on bio-
diversity, water quality, or flood risks (Alberti, 2005), most ur-
ban growth models are based on cell-level conversions and have
not focused on generating realistic spatial structures across scales
(Jantz and Goetz, 2005). To bridge the gap between cell- and
object-based representation, we developed FUTURES (FUTure
Urban-Regional Environment Simulation), a patch-based, multi-
level modeling framework for simulating the emergence of land-
scape spatial structure in urbanizing regions (Meentemeyer et al.,
2013). The FUTURES model was successfully applied in sev-
eral cases including a study of land development dynamics in the
rapidly expanding metropolitan region of Charlotte, North Car-
olina (Meentemeyer et al., 2013) and an analysis of the impacts
of urbanization on natural resources under different conservation
strategies (Dorning et al., 2015). Most recently, FUTURES was
coupled with ecosystem services models to examine the impacts
of projected urbanization and urban pattern on several ecosystem
services and their trade-offs (Shoemaker, 2016, Pickard et al., in
prep.).
In order to study the complex interactions between human and
natural systems, interdisciplinary researchers are coupling exist-
ing simulation models. Land change modeling plays often a cru-
Corresponding author
cial role in these coupled models. Previous case studies with FU-
TURES have demonstrated that the model can be applied to a
wide range of cases with different study systems and aims. The
initial implementation of model, however, was a prototype that
was not ready to be shared with scientific community. The model
accumulated too much “technical depth” (Easterbrook, 2014) dur-
ing its initial development, making it difficult to add new fea-
tures or run the simulation at larger scales. In order to continue
adding new capabilities to FUTURES and to promote its usage
both inside and outside of the land use community, we decided to
revise the implementation of the FUTURES model and develop
a new version which would be (a) more efficient and scalable,
(b) as easy to use as possible for a wider audience and (c) fully
open source and maintainable in the long run. To achieve these
goals we decided that instead of keeping FUTURES as a stan-
dalone application, we would take advantage of existing geospa-
tial software and integrate FUTURES into open source GRASS
GIS (Neteler and Mitasova, 2008). By using GRASS GIS’ effi-
cient geospatial libraries we can develop better and higher-level
code. Providing open source software to the scientific commu-
nity entails more than just releasing the actual code – documen-
tation, tutorials, installation instructions, binaries and support are
also needed and require considerable effort. Without this effort,
models cannot be practically used by other researchers. By us-
ing GRASS GIS’ existing infrastructure we could focus on de-
veloping the actual materials instead of managing our own server
infrastructure.
In this article we present a new version of the FUTURES urban
growth model that is available as the r.futures module set from the
GRASS GIS add-on repository. This new version of FUTURES
streamlines data processing, provides opportunities to study ur-
banization on mega-regional scales, and allows for more repro-
ducible research in the land change community. We demonstrate
this new version of FUTURES with a case study of the Asheville
metropolitan area in North Carolina, USA.
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B7, 2016
XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XLI-B7-953-2016
953
2. FUTURES MODEL
FUTure Urban-Regional Environment Simulation is a stochastic,
patched-based model for projecting landscape patterns of urban
growth (Meentemeyer et al., 2013). FUTURES has a modular
structure consisting of 3 main submodels: DEMAND, POTEN-
TIAL and PGA (patch-growing algorithm), see Figure 1. Land
conversion is driven by projected population demand computed
by the DEMAND submodel, and is spatially defined by a prob-
ability surface derived by the POTENTIAL submodel from mul-
tiple environmental and socio-economic predictors. The popu-
lation demand and the effects of land change drivers can vary in
space by subregions, such as jurisdictional units, allowing projec-
tions across heterogeneous landscape. FUTURES main strength
lies in realistically modeling the spatial structure of urban change
by growing patches that are parameterized by size and compact-
ness and calibrated using historical data. For a detailed explana-
tion of FUTURES’ components, please refer to Meentemeyer et
al. (2013).
PGA
DEMAND
POTENTIAL
Results
Environmental,
infrastructural,
socio-economic
predictors
Land cover maps
through time
Subregions
Historical
population data
Population
projections
where to build?
how much land?
patch size & shape?
Figure 1: Simplified schema of FUTURES conceptual model
with inputs and outputs in gray and submodels in yellow
The original implementation of FUTURES consisted mainly of
the patch-growing algorithm, a standalone program written in a
mixture of C and C++. The PGA program itself utilized ineffi-
cient algorithms and required raster data in ASCII format as input
leading to very slow initialization. The DEMAND submodel was
computed in a spreadsheet and POTENTIAL coefficents were de-
rived using R statistical software. No official implementation
of these submodels existed so each researcher developed a dif-
ferent workflow. This made it difficult for peers to verify each
other’s work. Several scripts for calibrating patch characteris-
tics derived with FRAGSTATS (McGarigal et al., 2012) existed,
however these tools were written in an unnecessarily low-level
language for a specific case using the author’s directory layout.
When revising the original implementation of FUTURES we iden-
tified several issues which needed to be addressed. First, it is im-
portant to follow best practices for scientific computing (Wilson
et al., 2014) including use of a versioning system, writing doc-
umentation and testing. We also wanted to minimize tasks that
had been done manually in order to make the process more ef-
ficient and avoid errors that are often difficult to detect. When
automating tasks we had to compromise between the flexibility
and simplicity of the workflow. We also focused on making FU-
TURES scalable so that it can run large scale applications at a
relatively fine spatial resolution. Finally, we designed FUTURES
to be more user-friendly and easy to test so that anyone can con-
fidently apply it to their research.
3. INTEGRATION IN GRASS GIS
GRASS GIS has had a long history as a platform for scientific
models (Chemin et al., 2015). As an open source GIS used by
researchers worldwide and one of the founding projects of OS-
Geo, GRASS GIS provides a stable environment for the devel-
opment of scientific models for studying problems from various
domains including geomorphology, hydrology, planetary science,
landscape ecology, hazard mapping, archaeology, renewable en-
ergy and transportation. Thanks to the numerous scientist and
developers who have been involved, GRASS GIS today provides
a large spectrum of geospatial modules ranging from basic GIS
functionality to highly specialized models. Most of the special-
ized tools are not part of standard GRASS GIS installations, but
are easily accessible from the add-on repository.
There were multiple reasons for our decision to integrate FU-
TURES into GRASS GIS as an add-on. Some of these reasons
were specific to FUTURES, but others apply to any spatial, sci-
entific model. Integrating a model into GIS gives both users and
developers a wide array of standard geospatial tools that simplify
the implementation of a model, and streamline pre- and post-
processing and visualization. GRASS GIS provides model devel-
opers a raster library for highly efficient data reading and writ-
ing. This means that FUTURES no longer has to read in ASCII
files, significantly reducing time needed for initialization. Fur-
thermore, raster data from FUTURES simulations are efficiently
compressed. Despite ever increasing disk space, it is still quite
important to reduce the file size, especially for stochastic spatio-
temporal simulations, which typically generate huge datasets. In
order to achieve the best speed performance, most GRASS GIS
functionality is implemented in C. Because of this, we could eas-
ily integrate FUTURES code, written in a mix of C and C++,
without major rewriting. For portability reasons we later decided
to use the C99 standard. While C and C++ are the preferred lan-
guages for computationally expensive algorithms, GRASS GIS
also supports Python as the primary scripting language. This
is crucial because FUTURES had many steps of data prepara-
tion that we were easily able to automate using Python scripting.
Model developers can appreciate GRASS GIS’ automatic gener-
ation of command line interfaces, Python interfaces and graph-
ical user interfaces (GUI). Simply by defining options in C or
Python modules we can call the same module from a GUI dia-
log, a Python script or a Bash script. A graphical interface makes
FUTURES easy to use, especially for users on the Windows plat-
form. A Python or Bash interface, however, is needed for more
advanced applications such as running FUTURES in parallel on a
high performance computer. GRASS GIS provides infrastructure
for publishing and distributing models to users on all major plat-
forms. Models and tools in GRASS GIS’s Add-on repository1
can be easily browsed and installed with their documentation, re-
lieving researchers of the burden of maintaining such infrastruc-
ture.
3.1 Implementation
We implemented FUTURES as a set of GRASS GIS modules
starting with a common prefix r.futures:
r.futures.demand extrapolates the area of developed land from
population trends and projections.
r.futures.devpressure computes the development pressure pre-
dictor.
r.futures.potential models the development probability sur-
face through multi-level logistic regression.
r.futures.calib calibrates patch sizes and shapes.
r.futures.pga simulates urban development using the patch
growing algorithm.
1https://grass.osgeo.org/download/addons/
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B7, 2016
XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XLI-B7-953-2016
954
In addition, we implemented the add-on r.sample.category needed
for the workflow. Since its functionality is not specific to FU-
TURES, we kept it separate. All of these add-ons can be conve-
niently installed from GRASS GIS using the GUI or command
line2. Each individual add-on has a manual page accessible both
online and offline. Figure 2 shows FUTURES workflow and the
inputs needed for each tool. In the following sections we de-
scribe the developed tools, their functionality and implementation
in GRASS GIS.
r.futures.demand
r.futures.potential
r.futures.devpressure
r.sample.category
subregions
landuse
predictors
subregions
landuse
predictors
subregions
landuse
population
landuse
r.futures.pga
r.futures.calib
Figure 2: Diagram of FUTURES workflow showing how are
r.futures modules (yellow boxes) chained and what are their
input data (grey boxes). As indicated by the light yellow box,
module r.futures.calib calls r.futures.pga.
3.1.1 r.futures.demand Based on historical land development
and population growth, the DEMAND submodel (implemented
as r.futures.demand) projects the rate of per capita land consump-
tion for each year of the simulation and each subregion. This
Python module uses GRASS GIS Python Scripting Library and
the NumPy, SciPy and matplotlib libraries for scientific comput-
ing to approximate the relation between population and land con-
sumption with a statistical model described by a linear, logarith-
mic or exponential curve. For example, a logarithmic relation
means that a growing population requires less developed land per
person over time. With enough data points, the module can select
the best curve for each subregion based on residuals. The primary
outputs are plain text files with tab-separated values representing
the number of cells to be converted to developed land each year
for each subregion. The module plots the resulting curves and
projected points for each subregion (Figure 3) so that the results
can be visually inspected. The module r.futures.demand provides
a fast way to estimate the land demand given a large number of
subregions with diverse population trends and thus allows us to
quickly explore different population scenarios.
3.1.2 r.futures.devpressure Development pressure is one of
the most important predictors of where development is likely to
happen. For each cell it is computed as a distance decay func-
tion of neighboring developed cells (Meentemeyer et al., 2013).
Compared to the tool previously used for computing development
pressure, the new Python module r.futures.devpressure provides a
faster and more efficient implementation by taking advantage of
the existing GRASS GIS module r.mfilter written in C for moving
window analysis with custom designed matrix filters. By precom-
puting the matrix of distances we avoid repeated distance compu-
tations resulting in faster processing. Because the new implemen-
tation is less memory intensive it can be used for larger regions
than the previous tool.
3.1.3 r.futures.potential uses multilevel logistic regression to
model development suitability based on environmental, infras-
tructural, and socio-economic predictors such as distance to roads
or topographic slope. We randomly sample these predictors and
2g.extension r.futures
180000
200000
220000
240000
260000
280000
300000
320000
population
225000
230000
235000
240000
245000
250000
255000
260000
265000
270000
developed cells
37021, RMSE: 1188.867
y= 2.505 + 0. 257 ln( x1.382)
Figure 3: An example of r.futures.demand output plot showing
the logarithmic relation between population and land
consumption for the county with ID 37021. Observed data are
showed as blue dots and predicted data as circles.
the observed change from undeveloped to developed cells to es-
timate the coefficients of the multilevel logistic regression. The
core of this module is a script in the R language (R Develop-
ment Core Team, 2008), which uses the package lme4 (Bates et
al., 2015) for fitting generalized linear mixed-effects models and
the package MuMIn (Barto, 2015) for automatic model selection.
The output file is a plain text file with tab-separated regression
coefficients. This script is wrapped in Python for more seamless
processing and chaining of modules. The coupling between R,
Python and GRASS GIS is intentionally very loose to make the
workflow possible in the Windows environment where some of
the other, more elegant, options such as rpy23are complicated to
use. We performed stratified sampling of observed new develop-
ment and predictors using GRASS GIS add-on r.sample.category.
Although we developed this add-on for urban growth modeling
with FUTURES, its application is much broader. In order to en-
courage its use in other applications we made it a general module
rather than making it part of the r.futures tool set.
3.1.4 r.futures.pga is the main engine of FUTURES – it sim-
ulates urban growth using inputs from the DEMAND and PO-
TENTIAL submodels. The patch growing algorithm (PGA) sto-
chastically allocates a seed for new development across the de-
velopment suitability surface, challenges the seed by comparing
it with a random number between 0 and 1, and then grows a
discrete patch from the seed if it survives (Meentemeyer et al.,
2013). This process repeats until the number of converted cells
specified by DEMAND is met. The development pressure predic-
tor and then the development suitability values are updated based
on the newly developed cells. (The development suitability is
computed internally from predictors and regression coefficients
supplied by POTENTIAL.) We kept the original patch growing
algorithm, but significantly improved its implementation to make
it faster, more memory efficient and simpler to use. We replaced a
custom, undocumented configuration file with a standard module
interface usable from GUI or the command line, and restructured
the input and output parameters and their names so that they are
easy for users to understand. We used efficient GRASS GIS li-
braries for reading and writing raster data, which minimized the
3rpy2 is a Python package for using R from Python (http://rpy2.
bitbucket.org/)
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B7, 2016
XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XLI-B7-953-2016
955
time needed to initialize the simulation. FUTURES now reads
rasters in GRASS’s native format instead of ASCII files. This
decreased the time needed for model initialization from several
minutes to several seconds for a region with tens of millions of
cells. Furthermore, we replaced the static allocation of internal
structures with dynamic allocation and reduced the overall mem-
ory requirements so that FUTURES could run on large regions
with tens or hundreds of counties as well as smaller areas like our
case study. Finally, through the use of appropriate programming
techniques, such as binary search, we significantly increased the
speed of the algorithm.
3.1.5 r.futures.calib We developed a dedicated Python mod-
ule for calibrating patch sizes and shapes that runs the module
r.futures.pga with different combinations of patch parameters and
outputs a table with scores for each combination of patch param-
eters. The simulation is run multiple times for each combination
to account for the stochasticity of the model. To speed up the
calibration process r.futures.calib can take advantage of multiple
computer cores.
4. CASE STUDY
To demonstrate how the new FUTURES framework can be used
to simulate urban growth, we present a case study for Asheville
metropolitan area located in the Blue Ridge Mountains in the
west of North Carolina, USA. The region consists of five counties
with total area of 6,271 km2and around 477,000 people based on
2014 population estimates. It is characterized by rapid popula-
tion growth around Asheville, the largest city of the region. New
development is constrained by the steep mountainous terrain and
large national and state parks. We simulate urban growth from
2012 to 2030 using publicly available data, including the USGS’s
National Land Cover Database (NLCD) (Homer et al., 2015, Fry
et al., 2011, Homer et al., 2007, Fry et al., 2009), past estimates
and future projections of county populations (NCOSBM, 2015),
boundaries and roads provided by the United States Census Bu-
reau’s database (TIGER) and a digital elevation model from the
National Elevation Dataset (NED) distributed by the USGS.
4.1 Approach
There are several steps required to run the FUTURES simulation:
Preprocess the data.
Estimate per capita land consumption controlling the total
area of converted land.
Derive the development suitability statistical model to con-
trol where the new development happens.
Calibrate patch size and shape.
Run the urban growth simulation.
4.1.1 Data preparation The core input data for urban growth
modeling with FUTURES is a timeseries of land cover maps,
which can be derived by various methods from satellite imagery.
In this study we used the 2001, 2006 and 2011 NLCD Land
Cover products and 1992/2001 Retrofit Land Cover Change prod-
uct to derive a 30-meter binary representation of developed areas.
We excluded national and state parks, water bodies and wetlands
from further analysis. We used NLCD products that are avail-
able for the contiguous USA so that this study and its workflow
would be easier to reproduce and apply to other study areas. We
obtained population statistics from the North Carolina Office of
State Budget and Management, which are based on 2000 and
2010 censuses and include past as well as future estimates of pop-
ulation per county for each year up to 2035. Data for the 5 coun-
ties studied were extracted and formatted as a comma-separated
values (CSV) file.
Figure 4: 2011 land cover (Homer et al., 2015) and protected
areas (Anderson and Sheldon, 2011) in the Asheville
metropolitan area in the west of North Carolina, USA. Inset A is
used in Figure 5.
4.1.2 DEMAND We derived the relation between population
and land consumption from the series of binary rasters of de-
veloped areas and population statistics to model how much land
will be developed each year of the simulation. Using the mod-
ule r.futures.demand we explored different curve fitting methods
and derived the per capita land consumption from period 1992
through 2011, which was characterized by population growth with
decreasing demand for land per person over time. We expect sim-
ilarly low rates of per capita land consumption in the following
years because development is restricted by the mountainous ter-
rain and large protected areas. Based on RMSE and visual inspec-
tion of the plots created by r.futures.demand we selected either
linear or logarithmic relations for each county, where the function
coefficients were automatically determined using linear regres-
sion and non-linear least squares optimization in r.futures.demand
(Figure 3).
4.1.3 POTENTIAL We used multilevel logistic regression to
predict where new development happens based on environmental,
infrastructural and socio-economic site suitability predictors. Us-
ing r.sample.category we sampled predictors on 8000 randomly
selected locations and estimated the model coefficients using the
R package lme4 integrated into the module r.futures.potential.
The sample points were stratified by the response variable where
new sites developed since 1992 have a value of 1 and sites that
are undeveloped in 2011 have a value of 0. We included counties
as the group level indicator in the multilevel model to account
for differences across jurisdictional boundaries. From the initial
list of hypothesized predictors (slope, distance to water, protected
areas, interchanges, travel time to cities and towns, forest occur-
rence and road density) we identified a set of predictors (Table
1) resulting in a model with the lowest AIC (Akaike information
criterion) score. We verified the robustness of the selected pre-
dictors by repeating the random sampling and model selection
process multiple times. In addition to these predictors, we in-
cluded also development pressure, a special, dynamic predictor
that is updated during the simulation based on new simulated de-
velopment to enable positive feedback. We computed the initial
development pressure raster with r.futures.devpressure; its subse-
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B7, 2016
XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XLI-B7-953-2016
956
undeveloped projected development
developed until 2011 protected areas and water roads 5 km
a) sprawl b) status quo c) inll
AshevilleAshevilleAshevilleAsheville AshevilleAsheville
Figure 5: Results of three realizations of multiple stochastic runs with different scenarios. Depending on the scenario, simulated
development is more diffuse (a) or more compact (c).
quent updates are performed in memory during the simulation.
Predictors Estimate*Std. Error
Intercept (varies by county) -2.593 0.269
Development pressure 0.058 0.005
Road density 0.118 0.007
Percentage of forest -0.013 0.002
Distance to protected areas -0.140 0.039
Distance to water bodies -0.148 0.022
* all P-values <0.001
Table 1: List of selected predictors and estimated coefficients for
site suitability model
4.1.4 Patch calibration Prior to running the urban growth
simulation implemented in r.futures.pga we calibrated the input
patch compactness and size to match the simulated patterns with
the observed patterns from 1992 to 2011. Since calibration is
a time consuming process, we ran the module r.futures.calib for
Buncombe county and applied the results to the rest of our study
region. We choose Buncombe County which includes the city
of Asheville because it is where most new development will oc-
cur. For each combination of patch parameters we compared the
patch characteristics averaged from 20 runs of the urban growth
simulation with the known patches. Based on the score we se-
lected patch parameters resulting in high compactness which is
expected for mountainous regions.
4.1.5 Urban growth simulation Having collected all neces-
sary input data, we ran r.futures.pga with a 1 year time step until
2035 for the entire study region at 30 m resolution. To account for
different future policies regarding new development, we explored
scenarios altering the site suitability to encourage infill or sprawl
by changing incentive power parameter of r.futures.pga. This
value transforms the probability pa cell is developed to pxwhere
x= 1 represents status quo, higher values of xresult in infill and
lower values in sprawl. In addition to the status quo we simulated
scenarios with xequals 0.25, 0.5, 2 and 4. We repeated each
scenario 50 times to account for the model’s stochastic behavior.
4.2 Results
The resulting development patterns of three realizations of the
random runs are visible in Figure 5 for the status quo, infill sce-
nario (x= 4) and sprawl scenario (x= 0.25). The simulated
patches realistically mimic the current patches of development
in shape and size and are mostly, but not exclusively adjacent to
roads as expected. Furthermore, we post-processed the results to
study how different urban growth policies influence the loss of
forest and agricultural land in the Asheville area (Figure 6) by
averaging the loss of both land use categories over the 50 runs. In
all scenarios, forest is more affected by future development than
farmland. The extreme case of urban sprawl results in twice as
much forest as farmland being developed. Status quo scenario
leads to the smallest difference between the areas converted to
forest and farmland. Interestingly, infill scenario develops the
forested area in a similar way as sprawl does, which is not surpris-
ing considering developed areas are largely surrounded by forest
patches.
0.25 0.5 1 24
0
2
4
6
8
10
12
14
16 forest farmland
inllsprawl status quo
Converted area in km2
Figure 6: Area in km2of converted land from forest (green) and
farmland (yellow) to urban differs for urban sprawl and infill
scenarios. Numbers 0.25 to 4represent the exponent xwhich
transforms development probability pto px.
Table 2 shows the computational resources necessary for run-
ning this case study and compares the time and memory require-
ments with the original implementation of FUTURES. Note that
our study area is fairly small (12 million cells) and when applied
to larger regions with more projected development, the expected
speed gain is even more significant as we changed the complexity
of one of the core algorithms from linear to logarithmic. Because
the individual stochastic runs are independent of each other this
simulation is an “embarrassingly parallel” problem (Herlihy and
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLI-B7, 2016
XXIII ISPRS Congress, 12–19 July 2016, Prague, Czech Republic
This contribution has been peer-reviewed.
doi:10.5194/isprsarchives-XLI-B7-953-2016
957
Shavit, 2012) in which the computation can easily be distributed
across multiple computer cores.
FUTURES version memory 1 run all runs (250)
original 1.7 GB 60 s 4 h 10 min
r.futures 0.86 GB 19 s 1 h 20 min
Table 2: Time and memory needed to run the simulations with
the old version of FUTURES and the new r.futures implemented
in GRASS GIS on a laptop with 64-bit Ubuntu 14.04 LTS, Intel
Core i7-4760HQ @2.10GHz using 1 CPU and running on
external hard drive.
The input data and instructions to run the model are available as
part of material developed for the US-IALE 2016 Annual Meet-
ing workshop on FUTURES4.
5. DISCUSSION
The new FUTURES framework is split into independent GRASS
GIS modules so that the modeling workflow is flexible and ex-
tendable. By using standardized inputs and outputs (raster lay-
ers and CSV files) and described interface we allow FUTURES’
users to replace DEMAND and POTENTIAL implementations
by their own tools, which may be better suited to the character-
istics and datasets available for their study systems. We ran all
previous studies on county level at 30 m resolution. FUTURES,
however, can be applied to larger or smaller scales as long as
there is data available and the patch characteristics are properly
calibrated. Future research will explore nested scales in order to
address the different scales of the population data and the spatial
drivers of land change.
6. CONCLUSION
We presented a new, open source version of the FUTURES ur-
ban growth model that is integrated into GRASS GIS, opening
new possibilities for environmental scientists and urban planners
to project and understand the impacts of urbanization at relevant
ecological and decision-making scales. Integration into GRASS
GIS allowed us to make FUTURES more efficient, simple to use
and transparent. With documented code running on all platforms,
FUTURES can now be easily tested and applied to study sites at
local to megaregional scales. We illustrated how FUTURES can
be used in a small case study of the Asheville metropolitan area.
We also provided the instructions and data needed to reproduce
this study as a step towards more reproducible research in land
change science.
ACKNOWLEDGEMENTS
We would like to thank Monica Dorning and Douglas Shoemaker
for discussing with us the original model implementation, and
Brian Pickard and Georgina Sanchez for testing new FUTURES
implementation.
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Two-dimensional dam break flooding simulation: a GIS embedded approach. - Natural Hazards 61(3):1143-1159. [3] Chemin, Y.H., 2012. A Distributed Benchmarking Framework for Actual ET Models. In Evapotranspiration - Remote Sensing and Modeling, Intech (Eds). [4] Chemin, Y. H. , 2014. Remote Sensing Raster Programming, 3rd Ed., Lulu (Eds). [5] Di Leo, M., de Rigo, D., Rodriguez-Aseretto, D., Bosco, C., Petroliagkis, T., Camia, A., San-Miguel-Ayanz, J., 2013. Dynamic data driven ensemble for wildfire behaviour assessment: A case study. IFIP Advances in Information and Communication Technology, vol. 413, pp. 11-22, 2013, ISSN:1868-4238. Special issue: "Environmental Software Systems. Fostering sharing information". [6] García, M., Villagarcía, L., Contreras, S., Domingo, F. & Puigdefábregas, J. (2007). Comparison of three operative models for estimating the surface water deficit using aster reflective and thermal data, Sensors 7(6): 860–883. [7] Gao, Y. & Long, D. ,2008. 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[14] North Carolina State University, Geospatial Modeling Course, GIS/MEA582, 2015. http://courses.ncsu.edu/ [15] Petras, V., Gebbert, S., 2014. Testing framework for GRASS GIS: ensuring reproducibility of scientific geospatial computing. Poster presented at: AGU Fall Meeting, December 15-19, 2014, San Francisco, USA. [16] Petrasova, A., Harmon, B., Petras, V., Mitasova, H., 2014. GIS-based environmental modeling with tangible interaction and dynamic visualization. In: Ames, D.P., Quinn, N.W.T., Rizzoli, A.E. (Eds.), Proceedings of the 7th International Congress on Environmental Modelling and Software, June 15-19, San Diego, California, USA. ISBN: 978-88-9035-744-2 [17] Raghavan, v., Choosumrong, S., Yoshida, D., Vinayaraj, P., 2014. Deploying Dynamic Routing Service for Emergency Scenarios using pgRouting, GRASS and ZOO. In Proc. of FOSS4G Europe, Jacobs University, Bremen, Germany, July 15-17, 2014. [18] Rocchini, D., Neteler, M. ,2012. 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