Modeling Land Use Change:What can we learn from

geographers?

Keith C. ClarkeProfessor and Chair

Department of Geography/NCGIAUniversity of California

Santa Barbara

What is land use? (cover vs. use)

What is land use change?

Land use change in action

Land use transitions are slow and cumulative

Land use mapping

Land use change modeling variables

Driver (s) (Derive e.g. Burgi & Turner Ecosystems, 2002)

State probabilities (static, dynamic)Class magnitudesSpatial autocorrelation(s)Feedbacks

Change in space

Change by class: The transition matrix

Green Orange Yellow

Green 25 (1.0) 0 (0.0) 0 (0.0)

Orange 0 (0.0) 47 (0.98) 1 (0.02)

Yellow 0 (0.0) 0 (0.0) 47 (1.0)

Change sequences

Wildland to agricultureForest to agricultureWetland to agricultureAgriculture to urbanResidential to commercialAgriculture to forest

National trends

Major problemsConsistency in land use classes over timeMisregistration (false change)Scale, generalization differencesGetting long time seriesScaling up and downUsing remotely sensed data (80%)Accuracy, timeliness and costCalibration and performance

“Classic” models: Bid rent

Johann Heinrich Von Thunen 1783-1850Alonso (1964) and Muth (1969) extensions“Highest and best use”

Computer-based modeling:False startsLee, D. B. (1973) Requiem for Large-Scale

Models, Journal of the American Institute of Planners 39, pp. 163-178

Seven deadly sins of large scale models • Hypercomprehensiveness • Grossness • Hungriness • Wrongheadedness • Complicatedness • Mechanicalness • Expensiveness

So what has changed?

Better dataIKONOS, Landsat 7Comprehensive land use mapping programs

(e.g.NLCDB)Better time series

Landsat 1972-2002CORONA

Better computers (Geocomputation, tractability)

GISMore varied (better?) models

Types of Models

Economic theoryGIS-based allocationCellular automataAgent basedIntegrated modelingLink to decision-making

USFS Model inventoryA Review and Assessment of Land-Use Change Models

Dynamics of Space, Time, and Human ChoiceC. Agarwal, G. L. Green, M. Grove, T. Evans, and C. Schweik

(2000)

Model scaleTime step and durationResolution and extentAgent and domain

Model ComplexityTemporal complexitySpatial complexityHuman decision-making complexity

Models Surveyed

1. General Ecosystem Model (GEM) (Fitz et al. 1996)

2. Patuxent Landscape Model (PLM) (Voinov et al. 1999)

3. CLUE Model (Conversion of Land Use and Its Effects) (Veldkamp and Fresco 1996a)

4. CLUE-CR (Conversion of Land Use and Its Effects – Costa Rica) (Veldkamp and Fresco 1996b)

Models (2)

5. Area base model (Hardie et al. 1997)6. Univariate spatial models (Mertens et al.

1997)7. Econometric (multinomial logit) model

(Chomitz et al. 1996)8. Spatial dynamic model (Gilruth et al. 1995)9. Spatial Markov model (Wood et al. 1997)

Models (3)10. CUF (California Urban Futures) (Landis

1995, Landis et al. 1998) [CUF II]11. LUCAS (Land Use Change Analysis System)

(Berry et al. 1996)12. Simple log weights (Wear et al. 1998)13. Logit model (Wear et al. 1999)14. Dynamic model (Swallow et al. 1997)15. NELUP (Natural Environment Research

Council [NERC]–Economic and Social Research Council [ESRC]: NERC/ESRC Land Use Programme [NELUP]) (O’Callahan 1995)

Models (4)

16. NELUP - Extension, (Oglethorpe et al. 1995)

17. FASOM (Forest and Agriculture Sector Optimization Model) (Adams et al. 1996)

18. CURBA (California Urban and Biodiversity Analysis Model) (Landis et al. 1998)

19. Cellular automata model (Clarke et al. 1998, Kirtland et al. 2000) [SLEUTH]

Model #7 Entry

Model Name/Citation Chomitz et al. 1996 Model Type Components/Econometric (multinomial logit)

model Modules Single module, with multiple equations What It Explains /Dependent Variable

Predicts land use, aggregated in three classes: Natural vegetation Semi-subsistence agriculture Commercial farming

Other Variables Strengths Soil nitrogen, Available phosphorus, Slope, Ph, Wetness, Flood

hazard, Rainfall, National land, Forest reserve, Distance to markets, based on impedance levels (relative costs of transport), Soil fertility

Model #7 Entry (ctd)

Strengths Used spatially disaggregated information to calculate an

integrated distance measure based on terrain and presence of roads

Also, strong theoretical underpinning of Von Thunen’s model

Weaknesses Strong assumptions that can be relaxed by alternate

specifications. Does not explicitly incorporate prices.

Drivers in the 19 Models Population

SizeGrowthDensity

Returns to Land-Use (costs and prices) Job Growth Costs of Conversion Rent Collective Rule Making and Zoning Tenure

Drivers (2) Relative Geographical Position to Infrastructure:

Distance from Road Distance from Town/Market Distance from Village Infrastructure/Accessibility Presence of Irrigation Generalized Access Variable

Village Size Silviculture Agriculture Technology Level Affluence Human Attitudes and Values Food Security Age

Economic models

Alonso/Muth traditionMultinomial logitDifferential equationsSteady state/equilibrium models (e.g.

demand = supply)Externally determined empirical

relationshipsCan be stochastic

Economic-based modelsAssume bid rent or other land demandDerive formulae linking variables, space

and distance, often GIS operationCompute potential for each unit of

granularity (e.g. pixels, parcels, LDUs)Allocate exogenously determined growth

amount by land usePrioritize allocation by rank or

stochastically

GIS-based allocation

Use GIS to compute land transition potential (transition matrix)

Use spatial landscape metrics to predetermine allocations

Assign specific numbers of granules new land use types based on rules

Stop when allocation demands are metOften single time incrementExample is CUF II

Cellular Automata

Gridded worldCells have finite statesRules define state transitionsTime is incrementalCells are autonomous, act as agentsSelf-replicating machines: Von NeumannClassic example is Conway’s LIFE

Urban Cellular Automata

Cells are pixelsStates are land usesNeighborhood is definedTime is “units”, e.g. yearsRules determine growth and changeDifferent models have different rule setsMany models now developed, few tested

SLEUTH CA Growth Rules

Behavior typesSpontaneousNew spreading centersOrganicRoad influence

Land use change: Deltatron modelTight coupling

Spontaneous Growth

f (diffusion coefficient, slope resistance)

urban settlements may occur anywhere on a landscape

Creation of new spreading centers

f (spontaneous growth, breed coefficient, slope resistance)

Some new urban settlements will become centers of further growth. Others will remain isolated.

Organic Growth

f (spread coefficient, slope resistance)

The most common type of development occurs at urban edges and as in-filling

Road Influenced Growth

f (breed coefficient, road_gravity coefficient, slope resistance, diffusion coefficient)

Urbanization has a tendency to follow lines of transportation

SLEUTH rule sequence

T0 T1

spontaneousspreading

center organicroad

influenced deltatron

Behavior Rules

T0 T1

For i time periods (years)

spontaneousspreading

center organicroad

influenced deltatron

f (slope resistance, diffusion

coefficient)

f (slope resistance,

breed coefficient)

f (slope resistance,

spread coefficient)

f (slope resistance, diffusion coefficient,

breed coefficient,road gravity)

Patterns/process of land cover change

Introduction of new land cover type (invasion, diffusion)

Land cover class extension from edges (spread, contagion)

Perpetuation of change (lagged autocorrelation)

Deltatron Dynamics:

To/From Transition matrix Table of land cover class average slopes Urbanization drives change within the

model Urban (and others) invariant class

Land cover Delta-space

Deltatrons at work

“Bringer of change” (semi-independent agent) Placeholder of where and what type of land cover

transition took place during its lifetime Tracks how much time has passed since a change

has occurred (Lifetime) Enforces spatial and temporal auto-correlation of

land cover transitions by its life cycle

A Deltatron is:

Deltatron Land Cover Model

Phase 1: Create change

YEL 1.20%ORN 3.30%GRN 5.60%

Averageslope

For n newurban cells

select random pixel

change land cover

spreadchange

Select two land classes at random

Of the two:Find the land class

most similar to current slope

YEL ORN GRNYEL 0.9 0.05 0.05ORN 0.05 0.9 0.05GRN 0.1 0.1 0.8

Transition ProbabilityMatrix

Check the transition

probability

Create delta space

Deltatron Land Cover ModelPhase 2: Perpetuate change

YEL ORN GRNYEL 0.9 0.05 0.05ORN 0.05 0.9 0.05GRN 0.1 0.1 0.8

Transition ProbabilityMatrix

search for change in the neighborhood find associated

land cover transitions

createdeltatrons

impose change inland cover

Age orkill

deltatrons

delta space

Prediction (the future from the present)

Probability Images

Alternate Scenarios

Land cover uncertainty

Agent based models

Agent is entity impacting change (e.g. farmer, business, household)

Agents are independent, spatially located, tracked separately, and updated

Behavior is preprogrammed, can also be reactive (e.g. to neighborhood or proximal agents)

Can have different types of agents with interactions (e.g. predator-prey)

Highly stochastic, performance hard to evaluate

(Bio) ComplexityNon-linear feedback (inter-agent

reactions)Multi-scale feedbacksBifurcations and phase changesCollapse/extinction possibleEmergenceEvolution and AlifeSWARM, STELLA, Ascape, etc.

Examples

Emilio Moran’s group (Indiana Univ)Change detection from remote sensingDemographic analysisParticipant interviews, decision-basedTest model in Midwest and AmazonAlso Dan Brown et al. at U Michigan

(SWARM)

Integrated Modeling

Multi-system modelingGeneralized modelsSupermodelsCoupled and linked models

GIS required for data handling, calibration, forecasts, etc

Coupling not so simple

GIS/EM: The integration challenge

Model integration

• Park & Wagner TGIS 1997• Isolated, Loose, Tight, Integrated

Share data from GISHave common input/output layersLink inputs to outputsHave a single user interface (UCIME)Hide the models from the userInteract via scenarios (integrate via

planning/decision-making process)

The UCIME Web Interface

Data/World Scenarios

Users/Decision MakersIndividual

Group

Convergence

Scenario as model/plan bridge

Model

Environmentalist

0

10

20

30

40

50

1 4 7 10 13 16 19 22 25

Question

Sce

nar

io D

ista

nce

Unrestrained

Follow Current

Road Growth

Growth Boundary

EnvironmentFriendly

No Commercial

Neutral Responses

0

10

20

30

40

501 4 7 10 13 16 19 22

Questions

Sce

nar

io D

ista

nce Unrestrained

Follow Current

Road Growth

Growth Boundary

Environment Friendly

No Commercial

Good scenario sets

Themes can be single or multipleHow many? 7+-2Relevance: Policy implicationComprehensive (drivers)DiverseCreative: Role for VisualizationTransparentCoherent: properly formulated and plausibleConsistent

Scenario Difference

Urban models in UCIME

Population density structureSLEUTH (Urban form and land use)SCOPEBy very loose coupling

HydrologyAir qualityWildfire hazard

ConclusionReviewed major concepts, problems in land use

change modelingCovered major contemporary types of modelsImportance of drivers and quantification of first

and second derivativesSets of models when integrated are more

powerful than when used alone, or when one metamodel is formulated

Users want credibility in modeling/ersScenarios are key to bridging models and views