The integration of land change modeling framework FUTURES ...Standalone vs. integrated into open...

The integration of land change modelingframework FUTURES into GRASS GIS 7

Anna Petrasova, Vaclav Petras, Douglas A. Shoemaker, MonicaA. Dorning, Ross K. Meentemeyer

NCSU OSGeo Research and Education LabCenter for Geospatial AnalyticsNorth Carolina State University

July 16, 2015

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FUTURES

FUTure Urban-Regional Environment Simulation (FUTURES)

stochastic, patch-based landchange model

simulates urban growth

model accounts for location,quantity, and pattern of change

incorporates positive feedbacks(new development attractsmore development)

allows spatial non-stationarity

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[Meentemeyer et al., 2013]

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The dark side of FUTURES

no documentation

know-how limited to certaincolleagues

code not reusable

different versions of code

no central storage for code

extensive manual data preparation

bad choice of programming language

https://xkcd.com/221

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https://xkcd.com/221

Open source approach

We hope FUTURES can make broader impact in land changecommunity...

...but more than just releasing the code online is needed.

We can take example from many open source projects which haveestablished ways and technologies for

collaboration

code development and distribution

providing documentation, user support

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Integration

Standalone vs. integrated into open source GIS:

+ all standard GIS tools andalgorithms available

+ distribution and installationacross all platform solved

+ established ways to providedocumentation

+ existing user base withsupport

– less flexibility in certainaspects

– need to learn to use theparticular GIS

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GRASS GIS

Why integrate FUTURES into GRASS GIS 7?

able to process large datasets

runs on HPC clusters

efficient I/O libraries

modular architecture – modules in C/C++ and Python, R

automatically generated CLI and GUI

addons hosted on OSGeo servers

easy installation: > g.extension r.futures

daily compiled binaries for Windows (thanks to M. Landa,FCE CTU in Prague)maintained by community and developers (API changes)

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FUTURES conceptual diagram

SUB-MODELS OUTPUTSINPUT DATA

DEVELOPMENTPOTENTIAL

[Location]

PATCH GROWINGALGORITHM[Spatial Structure]

HISTORICALDEVELOPMENT

PATTERNS

ENVIRONMENTAL,INFRASTRUCTURAL,SOCIO-ECONOMIC

PREDICTORS

HISTORICALPOPULATION

& PROJECTIONS

DEVELOPMENTOUTCO MESSize & Shape?

Where?

Howmanypeople?

LANDDEMAND

[Quantity]

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[Location]



PATTERNS


PREDICTORS


& PROJECTIONS


Where?

Howmanypeople?

LANDDEMAND

[Quantity]

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[Location]



PATTERNS


PREDICTORS


& PROJECTIONS


Where?

Howmanypeople?

LANDDEMAND

[Quantity]

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Patch Growing Algorithm

stochastic algorithm

converts land in discrete patches

implemented in C/C++

computationallydemanding

inputs are patchcharacteristics(distribution of patchsizes and compactness)derived from historicaldata

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[Location]



PATTERNS


PREDICTORS


& PROJECTIONS


Where?

Howmanypeople?

LANDDEMAND

[Quantity]

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DEMAND submodel

estimates the rate of per capita land consumption specific toeach subregion

extrapolates between historical changes in population andland conversion

Population

Develo

ped

are

a

1996

2006

2015

2025

2035 inputs are historicallanduse, population data,population projection

ordinary least squaresregression (linear orlogarithmic relationships)

informally implementedas R scripts and ArcGISworkflows

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[Location]



PATTERNS


PREDICTORS


& PROJECTIONS


Where?

Howmanypeople?

LANDDEMAND

[Quantity]

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POTENTIAL submodel

multilevel logistic regression for development suitability

accounts for variation among subregions (for example policiesin different counties)

inputs are uncorrelatedpredictors (distance to roadsand development, slope, ...)

original implementation

data preparation in ArcGISregression coefficients derivedin Rpotential and probabilitysurface recomputed aftereach step in the main C code

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Implementation overview



[Location]



PATTERNS


PREDICTORS


& PROJECTIONS


Where?

Howmanypeople?

LANDDEMAND

[Quantity]

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[Location]



PATTERNS


PREDICTORS


& PROJECTIONS


Where?

Howmanypeople?

LANDDEMAND

[Quantity]

r.futures.pga

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r.futures.pga

Originally standalone C++ code converted into GRASS GIS addon

efficient raster reading and writing

fixed segfaults

general cleanup (dead code), revised input options

r.futures.pga [-s] developed=name predictors=name[,name,...]

demand=name devpot params=name discount factor=value

...

incentive table=name [constrain weight=name] [random seed=value]

output=name [output series=basename] [--overwrite] [--help]

[--verbose] [--quiet] [--ui]

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r.futures.pga



fixed segfaults


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r.futures.pga



fixed segfaults


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r.futures.pga



fixed segfaults


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r.futures.pga



fixed segfaults


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[Location]



PATTERNS


PREDICTORS


& PROJECTIONS


Where?

Howmanypeople?

LANDDEMAND

[Quantity]

r.futures.pgar.futures.calib

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r.futures.calib

New addon written in Python for automated calibration of patchcharacteristics

provides optimal patch parameters for r.futures.pga bycomparing observed land change pattern with the simulatedpattern

runs in multiple processes

Higher compactness Lower compactness

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[Location]



PATTERNS


PREDICTORS


& PROJECTIONS


Where?

Howmanypeople?

LANDDEMAND

[Quantity]


r.futures.demand

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[Location]



PATTERNS


PREDICTORS


& PROJECTIONS


Where?

Howmanypeople?

LANDDEMAND

[Quantity]


r.futures.demand

r.futures.potential

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r.futures.potential & r.futures.demand

Implementation still needed for

r.futures.demand – Python and Numpy

r.futures.potential

R wrapped in Python to provide standard GRASS interfaceusing dredge from R package MuMIn1 for automated modelselection (read the dredge disclaimer before using)

DEMAND and POTENTIAL statistical models can be implementedin different ways, but we need reference implementation.

1http://cran.r-project.org/web/packages/MuMIn/MuMIn.pdf31 / 39

http://cran.r-project.org/web/packages/MuMIn/MuMIn.pdf




[Location]



PATTERNS


PREDICTORS


& PROJECTIONS


Where?

Howmanypeople?

LANDDEMAND

[Quantity]


r.futures.demand

r.futures.potential

r.sample.category

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[Location]



PATTERNS


PREDICTORS


& PROJECTIONS


Where?

Howmanypeople?

LANDDEMAND

[Quantity]


r.futures.demand

r.futures.potential

r.sample.category

r.futures.devpressure

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Data preprocessing for POTENTIAL

Two new Python modules needed for FUTURES datapreprocessing, but can be used for other applications:

r.sample.category

Create sampling points fromeach category in a raster map

source: r.sample.category manual page


Moving window computation ofdistance decay effect

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[Location]



PATTERNS


PREDICTORS


& PROJECTIONS


Where?

Howmanypeople?

LANDDEMAND

[Quantity]


r.futures.demand

r.futures.potential

r.sample.category


conceptual vs. userview

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Next steps

Finish r.futures.demans and r.futures.potentialMore documentation needed:

tutorial

sample dataset – Raleigh for consistency with GRASS GISNorth Carolina sample dataset

use publicly available data (National Land Cover Database)

NLCD 2011

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What do I need to run it?

1 Gather data (historical urban development, populationprojection, predictors)

2 Use a high-end workstation

3 Installation is simple, works on all platforms:

> g.extension r.futures

Code available online:trac.osgeo.org/grass/browser/grass-addons/grass7/raster/r.futures/

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trac.osgeo.org/grass/browser/grass-addons/grass7/raster/r.futures/

Take home message

1 Integrate your work into FOSS

2 Automate tasks

3 Document!

Thank you!

Contact: [email protected]

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[email protected]

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 LandscapeStructure 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 conservationplanning strategies

Landscape and Urban Planning, 136, 28–39.

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The integration of land change modeling framework FUTURES ...Standalone vs. integrated into open...

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