Open Source Approach to Urban Growth Simulation
Anna Petrasova, Vaclav Petras, Derek Van Berkel, Brendan Harmon, Helena Mitasova and Ross Meentemeyer
Urban growth models
simulating the future scenarios
Urban growth models: challenges
Can we understand the behavior of the model?
Can we make sure it is working as described?
FUTure Urban-Regional Environment Simulation
- stochastic, patch-based land change model
- simulates urban growth
- accounts for location, quantity,
and pattern of change
- positive feedbacks (new development
attracts more development)
- allows spatial non-stationarity
- realistic spatial pattern
- open source (+ dependencies)
- multilevel logistic regression for development suitability
accounts for variation among subregions (for example policies
in different counties)
- inputs are uncorrelated predictors (distance to roads and development, slope, ...)
- estimates the rate of per capita land consumption for
- extrapolates between historical changes in population and land conversion
- inputs are historical landuse, population data, population projection
- stochastic algorithm
- converts land in discrete patches
- inputs are patch characteristics (distribution of patch sizes and compactness)
derived from historical data
Open source FUTURES
To go beyond experimental prototype we needed to make FUTURES:
- more efficient and scalable
- as easy to use as possible for a wider audience
- open source and maintainable in the long run
⇒ new FUTURES GRASS GIS add-on
Why GRASS GIS?
For model developers:
- modular architecture: modules in C/C++ and Python
- all needed GIS functions at hand
- efficient I/O libraries
- able to process large datasets
- automatically generated CLI and GUI
- infrastructure for online manual pages
- daily compiled binaries for Windows
(thanks to M. Landa,
FCE CTU in Prague)
- maintained by community and developers
Why GRASS GIS?
For model users:
- graphical user interface
- scriptable (Bash, Python, R)
- easy installation:
> g.extension r.futures
available suite of tools for further analyses and visualization
(spatio-temporal analyses, animations)
r.futures.pga -s subregions=counties developed=urban_2011 \
output=final demand=demand.csv discount_factor=0.1 compactness_mean=0.1 \
devpot_params=potential.csv development_pressure=devpressure_0_5 \
n_dev_neighbourhood=30 gamma=0.5 patch_sizes=patches.txt num_neighbors=4 output=final
TUI: Tangible User Interface
Tangible Landscape couples a digital and a physical model through a continuous cycle of 3D scanning, geospatial modeling, and projection
Tangible Landscape: applications
- Meentemeyer, R. K., Tang, W., Dorning, M. A., Vogler, J. B.,
Cunniffe, N. J. and Shoemaker, D. A., 2013. FUTURES: Multilevel
Simulations of Emerging UrbanRural Landscape Structure
Using a Stochastic Patch-Growing Algorithm. Annals of the Association
of American Geographers 103(4), pp. 785–807.
- Dorning, M. A., Koch, J., Shoemaker, D. A. and Meentemeyer,
R. K., 2015. Simulating urbanization scenarios reveals tradeoffs
between conservation planning strategies. Landscape and Urban
Planning 136, pp. 28–39.
- Pickard, B. R., Van Berkel, D., Petrasova, A. and Meentemeyer,
R. K., in prep. Future patterns of urbanization reveal trade-offs
among ecosystem services.
- Petrasova, A., Petras, V., Van Berkel, D., Harmon, B. A., Mitasova, H., and Meentemeyer, R. K., 2016.
Open Source Approach to Urban Growth Simulation.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 953-959.