© k.fedra 20004 1 dynamic land use change modeling a simple spatial modeling application

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Dynamic Land Use Dynamic Land Use Change ModelingChange Modeling

Dynamic Land Use Dynamic Land Use Change ModelingChange Modeling

A simple spatial modeling A simple spatial modeling applicationapplication

A simple spatial modeling A simple spatial modeling applicationapplication

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Land Use Change modelingLand Use Change modelingLand Use Change modelingLand Use Change modeling

Land cover:Land cover:Natural land surface, vegetationNatural land surface, vegetation

Land use:Land use:Economic interpretation and Economic interpretation and

classification of land coverclassification of land cover

Land cover:Land cover:Natural land surface, vegetationNatural land surface, vegetation

Land use:Land use:Economic interpretation and Economic interpretation and

classification of land coverclassification of land cover

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Describes the change of land use or land Describes the change of land use or land cover over time.cover over time.

Provides inputs and boundary conditions Provides inputs and boundary conditions for other estimates such as:for other estimates such as:

• Population developmentPopulation development• Regional economy, GRPRegional economy, GRP• Resource requirements (water, energy)Resource requirements (water, energy)• Waste and environmental pollutionWaste and environmental pollution

Describes the change of land use or land Describes the change of land use or land cover over time.cover over time.

Provides inputs and boundary conditions Provides inputs and boundary conditions for other estimates such as:for other estimates such as:

• Population developmentPopulation development• Regional economy, GRPRegional economy, GRP• Resource requirements (water, energy)Resource requirements (water, energy)• Waste and environmental pollutionWaste and environmental pollution

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How to model land use change ?How to model land use change ?Divide the area under study into Divide the area under study into

small cells or units (a grid) of small cells or units (a grid) of homogeneous land use each;homogeneous land use each;

Describe the evolution of each Describe the evolution of each cell as a sequence of discrete cell as a sequence of discrete states (land use) over time.states (land use) over time.

How to model land use change ?How to model land use change ?Divide the area under study into Divide the area under study into

small cells or units (a grid) of small cells or units (a grid) of homogeneous land use each;homogeneous land use each;

Describe the evolution of each Describe the evolution of each cell as a sequence of discrete cell as a sequence of discrete states (land use) over time.states (land use) over time.

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Turing machine Turing machine (general purpose computer)(general purpose computer)

A.M.Turing (1936)A.M.Turing (1936)

John von Neumann (1950):John von Neumann (1950):Self-reproducing machinesSelf-reproducing machines

Cellular automata:Cellular automata:J.H. Conway: LIFE (game)J.H. Conway: LIFE (game)

Turing machine Turing machine (general purpose computer)(general purpose computer)

A.M.Turing (1936)A.M.Turing (1936)

John von Neumann (1950):John von Neumann (1950):Self-reproducing machinesSelf-reproducing machines

Cellular automata:Cellular automata:J.H. Conway: LIFE (game)J.H. Conway: LIFE (game)

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Cellular automata:J.H. Conway: LIFE (game)Cellular automata:J.H. Conway: LIFE (game)

Complex behavior with only two very Complex behavior with only two very simple rules:simple rules:

Survival:Survival: with 2 or 3 neighbors with 2 or 3 neighbors

Death: Death: less than2, more than 3less than2, more than 3

Birth:Birth: empty field with 3 neighbors/empty field with 3 neighbors/

Cellular automata:J.H. Conway: LIFE (game)Cellular automata:J.H. Conway: LIFE (game)

Complex behavior with only two very Complex behavior with only two very simple rules:simple rules:

Survival:Survival: with 2 or 3 neighbors with 2 or 3 neighbors

Death: Death: less than2, more than 3less than2, more than 3

Birth:Birth: empty field with 3 neighbors/empty field with 3 neighbors/

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Land Use Change modelingLand Use Change modelingLand Use Change modelingLand Use Change modelingContinuously moving GLIDERContinuously moving GLIDERContinuously moving GLIDERContinuously moving GLIDER

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• Simple dynamic modelSimple dynamic model

• Spatially distributedSpatially distributed

• Individual cells (objects, parcels Individual cells (objects, parcels of land) show SIMPLE behaviorof land) show SIMPLE behavior

• Complexity through interaction Complexity through interaction in space and timein space and time

• Simple dynamic modelSimple dynamic model

• Spatially distributedSpatially distributed

• Individual cells (objects, parcels Individual cells (objects, parcels of land) show SIMPLE behaviorof land) show SIMPLE behavior

• Complexity through interaction Complexity through interaction in space and timein space and time

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Basic modeling principle:Basic modeling principle:• State transition State transition (traffic light)(traffic light)

• Markov chain: a series of Markov chain: a series of random events or states from a random events or states from a given set, each determined only given set, each determined only by its predecessor by its predecessor (A.Markov, 1856-1922).(A.Markov, 1856-1922).

Basic modeling principle:Basic modeling principle:• State transition State transition (traffic light)(traffic light)

• Markov chain: a series of Markov chain: a series of random events or states from a random events or states from a given set, each determined only given set, each determined only by its predecessor by its predecessor (A.Markov, 1856-1922).(A.Markov, 1856-1922).

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Complexity through interaction in space Complexity through interaction in space and time, probabilistic state transitions:and time, probabilistic state transitions:

Transitions depend on neighborhood and Transitions depend on neighborhood and history: if each cell shares information history: if each cell shares information on all other cells intended transitions, it on all other cells intended transitions, it may change its own naïve strategy.may change its own naïve strategy.

EXAMPLE: urban development, building EXAMPLE: urban development, building apartment blocks apartment blocks (planning and zoning !)(planning and zoning !)

Complexity through interaction in space Complexity through interaction in space and time, probabilistic state transitions:and time, probabilistic state transitions:

Transitions depend on neighborhood and Transitions depend on neighborhood and history: if each cell shares information history: if each cell shares information on all other cells intended transitions, it on all other cells intended transitions, it may change its own naïve strategy.may change its own naïve strategy.

EXAMPLE: urban development, building EXAMPLE: urban development, building apartment blocks apartment blocks (planning and zoning !)(planning and zoning !)

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What makes land use change ?What makes land use change ?

1.1. Demographic developmentDemographic development

2.2. Economic, technological Economic, technological development development (incl. pollution, exhaustion)(incl. pollution, exhaustion)

3.3. Political development Political development (regional (regional planning, borders, war)planning, borders, war)

4.4. Climate change Climate change (suitability)(suitability)

What makes land use change ?What makes land use change ?

1.1. Demographic developmentDemographic development

2.2. Economic, technological Economic, technological development development (incl. pollution, exhaustion)(incl. pollution, exhaustion)

3.3. Political development Political development (regional (regional planning, borders, war)planning, borders, war)

4.4. Climate change Climate change (suitability)(suitability)

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Typical time scale:Typical time scale:DecadesDecades

Regulatory framework:Regulatory framework:Land use plan, zoningLand use plan, zoning

Building lawsBuilding laws

Ownership, property and inheritance Ownership, property and inheritance laws (agriculture)laws (agriculture)

Typical time scale:Typical time scale:DecadesDecades

Regulatory framework:Regulatory framework:Land use plan, zoningLand use plan, zoning

Building lawsBuilding laws

Ownership, property and inheritance Ownership, property and inheritance laws (agriculture)laws (agriculture)

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Model components:Model components:1.1. Land use classes, initial Land use classes, initial

conditions (map)conditions (map)2.2. Transition matrix (probabilities)Transition matrix (probabilities)3.3. RULES modifying the RULES modifying the

probabilities expressing global probabilities expressing global or regional constraintsor regional constraints

Model components:Model components:1.1. Land use classes, initial Land use classes, initial

conditions (map)conditions (map)2.2. Transition matrix (probabilities)Transition matrix (probabilities)3.3. RULES modifying the RULES modifying the

probabilities expressing global probabilities expressing global or regional constraintsor regional constraints

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CORINE land use classes:CORINE land use classes:

1.1.Urban fabric 1.1.Urban fabric

1.2 Industrial, commercial 1.2 Industrial, commercial

and transport units and transport units

1.3 Mine, dump and construction sites 1.3 Mine, dump and construction sites

1.4 Artificial non-agricultural 1.4 Artificial non-agricultural

vegetated areasvegetated areas


1.1.Urban fabric 1.1.Urban fabric

1.2 Industrial, commercial 1.2 Industrial, commercial

and transport units and transport units

1.3 Mine, dump and construction sites 1.3 Mine, dump and construction sites

1.4 Artificial non-agricultural 1.4 Artificial non-agricultural

vegetated areasvegetated areas

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1.1.Urban fabric 1.1.Urban fabric 1.1.1. Continuous urban fabric 1.1.1. Continuous urban fabric

1.1.2. Discontinuous urban fabric1.1.2. Discontinuous urban fabric


1.1.Urban fabric 1.1.Urban fabric 1.1.1. Continuous urban fabric 1.1.1. Continuous urban fabric

1.1.2. Discontinuous urban fabric1.1.2. Discontinuous urban fabric

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Land Use Change modelingLand Use Change modelingLand Use Change modelingLand Use Change modelingCORINE land use classes:CORINE land use classes:

2.1 Arable land 2.1 Arable land 2.2 Permanent crops 2.2 Permanent crops 2.3 Pastures 2.3 Pastures 2.4 Heterogeneous agricultural areas 2.4 Heterogeneous agricultural areas 3.1 Forest 3.1 Forest 3.2 Shrubs and/or herbaceous 3.2 Shrubs and/or herbaceous

vegetation associations vegetation associations 3.3 Open spaces with little or no vegetation3.3 Open spaces with little or no vegetation


2.1 Arable land 2.1 Arable land 2.2 Permanent crops 2.2 Permanent crops 2.3 Pastures 2.3 Pastures 2.4 Heterogeneous agricultural areas 2.4 Heterogeneous agricultural areas 3.1 Forest 3.1 Forest 3.2 Shrubs and/or herbaceous 3.2 Shrubs and/or herbaceous

vegetation associations vegetation associations 3.3 Open spaces with little or no vegetation3.3 Open spaces with little or no vegetation

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4.1 Inland wetlands 4.1 Inland wetlands

4.2 Coastal wetlands4.2 Coastal wetlands

5.1 Inland waters 5.1 Inland waters

5.2 Marine waters5.2 Marine waters


4.1 Inland wetlands 4.1 Inland wetlands

4.2 Coastal wetlands4.2 Coastal wetlands

5.1 Inland waters 5.1 Inland waters

5.2 Marine waters5.2 Marine waters

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Starting point: Starting point:

a historical a historical

land use map:land use map:Batroun/Tripoli,Batroun/Tripoli,

Northern LebanonNorthern Lebanon

Starting point: Starting point:

a historical a historical

land use map:land use map:Batroun/Tripoli,Batroun/Tripoli,

Northern LebanonNorthern Lebanon

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Shrubs, herbaceous veg. 17.0Shrubs, herbaceous veg. 17.0Permanent crops 14.7Permanent crops 14.7Forest, mixed 7.9Forest, mixed 7.9Heterogeneous agriculture: 2.9Heterogeneous agriculture: 2.9Urban fabric 2.8Urban fabric 2.8Open space, little veg. 2.7Open space, little veg. 2.7Mines. dumps, construction 0.8Mines. dumps, construction 0.8Industry, commerce, transport 0.4Industry, commerce, transport 0.4Coastal wetlands 0.1Coastal wetlands 0.1Unclassified 50.7Unclassified 50.7

Shrubs, herbaceous veg. 17.0Shrubs, herbaceous veg. 17.0Permanent crops 14.7Permanent crops 14.7Forest, mixed 7.9Forest, mixed 7.9Heterogeneous agriculture: 2.9Heterogeneous agriculture: 2.9Urban fabric 2.8Urban fabric 2.8Open space, little veg. 2.7Open space, little veg. 2.7Mines. dumps, construction 0.8Mines. dumps, construction 0.8Industry, commerce, transport 0.4Industry, commerce, transport 0.4Coastal wetlands 0.1Coastal wetlands 0.1Unclassified 50.7Unclassified 50.7

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The mechanisms:The mechanisms:

1.1. State transition matrix (simple State transition matrix (simple Markov Model)Markov Model)

2.2. Rules (shared information Rules (shared information described by first order described by first order production rules)production rules)

The mechanisms:The mechanisms:

1.1. State transition matrix (simple State transition matrix (simple Markov Model)Markov Model)

2.2. Rules (shared information Rules (shared information described by first order described by first order production rules)production rules)

© K.Fedra 20004

Land Use Change modelingLand Use Change modeling

state transition matrix:state transition matrix:

S1 S2 S3 S4S1 S2 S3 S4

S1 S1 0.74 0.74 0.01 0.20 0.05 0.01 0.20 0.05 ∑=1.0∑=1.0

S2 S2 0.01 0.01 0.980.98 0.00 0.010.00 0.01

S3 S3 0.00 0.02 0.00 0.02 0.930.93 0.05 0.05

S4 S4 0.10 0.01 0.02 0.10 0.01 0.02 0.870.87

state transition matrix:state transition matrix:

S1 S2 S3 S4S1 S2 S3 S4

S1 S1 0.74 0.74 0.01 0.20 0.05 0.01 0.20 0.05 ∑=1.0∑=1.0

S2 S2 0.01 0.01 0.980.98 0.00 0.010.00 0.01

S3 S3 0.00 0.02 0.00 0.02 0.930.93 0.05 0.05

S4 S4 0.10 0.01 0.02 0.10 0.01 0.02 0.870.87

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Land Use Change modelingLand Use Change modelingLand Use Change modelingLand Use Change modeling 1.1 1.2 1.3 1.4 2.1 2.2 2.3 2.4 3.1 3.2 3.3 4.1 4.21.1 1.2 1.3 1.4 2.1 2.2 2.3 2.4 3.1 3.2 3.3 4.1 4.2

1.11.1 0.860.86 0.05 0.02 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.00 0.00 0.05 0.02 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.00 0.001.21.2 0.01 0.01 0.950.95 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.001.31.3 0.05 0.10 0.05 0.10 0.850.85 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.001.41.4 0.01 0.00 0.00 0.01 0.00 0.00 0.990.99 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.002.12.1 0.10 0.05 0.00 0.10 0.10 0.05 0.00 0.10 0.400.40 0.10 0.05 0.10 0.05 0.05 0.00 0.00 0.00 0.10 0.05 0.10 0.05 0.05 0.00 0.00 0.002.22.2 0.10 0.05 0.05 0.05 0.05 0.10 0.05 0.05 0.05 0.05 0.560.56 0.05 0.05 0.01 0.01 0.02 0.00 0.00 0.05 0.05 0.01 0.01 0.02 0.00 0.002.32.3 0.10 0.05 0.01 0.05 0.01 0.10 0.10 0.05 0.01 0.05 0.01 0.10 0.550.55 0.10 0.01 0.01 0.01 0.00 0.00 0.10 0.01 0.01 0.01 0.00 0.002.42.4 0.10 0.05 0.01 0.05 0.05 0.10 0.11 0.10 0.05 0.01 0.05 0.05 0.10 0.11 0.500.50 0.01 0.01 0.01 0.00 0.00 0.01 0.01 0.01 0.00 0.003.13.1 0.05 0.02 0.01 0.01 0.05 0.01 0.01 0.01 0.05 0.02 0.01 0.01 0.05 0.01 0.01 0.01 0.820.82 0.00 0.01 0.00 0.00 0.00 0.01 0.00 0.003.23.2 0.05 0.02 0.01 0.04 0.00 0.01 0.02 0.02 0.03 0.05 0.02 0.01 0.04 0.00 0.01 0.02 0.02 0.03 0.800.80 0.00 0.00 0.00 0.00 0.00 0.003.33.3 0.06 0.02 0.01 0.03 0.01 0.01 0.03 0.02 0.00 0.01 0.06 0.02 0.01 0.03 0.01 0.01 0.03 0.02 0.00 0.01 0.800.80 0.00 0.00 0.00 0.004.14.1 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.01 0.00 0.01 0.03 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.01 0.00 0.01 0.03 0.940.94 0.00 0.004.24.2 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.980.98

1.1 1.2 1.3 1.4 2.1 2.2 2.3 2.4 3.1 3.2 3.3 4.1 4.21.1 1.2 1.3 1.4 2.1 2.2 2.3 2.4 3.1 3.2 3.3 4.1 4.2

1.11.1 0.860.86 0.05 0.02 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.00 0.00 0.05 0.02 0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.00 0.001.21.2 0.01 0.01 0.950.95 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.00 0.001.31.3 0.05 0.10 0.05 0.10 0.850.85 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.001.41.4 0.01 0.00 0.00 0.01 0.00 0.00 0.990.99 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.002.12.1 0.10 0.05 0.00 0.10 0.10 0.05 0.00 0.10 0.400.40 0.10 0.05 0.10 0.05 0.05 0.00 0.00 0.00 0.10 0.05 0.10 0.05 0.05 0.00 0.00 0.002.22.2 0.10 0.05 0.05 0.05 0.05 0.10 0.05 0.05 0.05 0.05 0.560.56 0.05 0.05 0.01 0.01 0.02 0.00 0.00 0.05 0.05 0.01 0.01 0.02 0.00 0.002.32.3 0.10 0.05 0.01 0.05 0.01 0.10 0.10 0.05 0.01 0.05 0.01 0.10 0.550.55 0.10 0.01 0.01 0.01 0.00 0.00 0.10 0.01 0.01 0.01 0.00 0.002.42.4 0.10 0.05 0.01 0.05 0.05 0.10 0.11 0.10 0.05 0.01 0.05 0.05 0.10 0.11 0.500.50 0.01 0.01 0.01 0.00 0.00 0.01 0.01 0.01 0.00 0.003.13.1 0.05 0.02 0.01 0.01 0.05 0.01 0.01 0.01 0.05 0.02 0.01 0.01 0.05 0.01 0.01 0.01 0.820.82 0.00 0.01 0.00 0.00 0.00 0.01 0.00 0.003.23.2 0.05 0.02 0.01 0.04 0.00 0.01 0.02 0.02 0.03 0.05 0.02 0.01 0.04 0.00 0.01 0.02 0.02 0.03 0.800.80 0.00 0.00 0.00 0.00 0.00 0.003.33.3 0.06 0.02 0.01 0.03 0.01 0.01 0.03 0.02 0.00 0.01 0.06 0.02 0.01 0.03 0.01 0.01 0.03 0.02 0.00 0.01 0.800.80 0.00 0.00 0.00 0.004.14.1 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.01 0.00 0.01 0.03 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.01 0.00 0.01 0.03 0.940.94 0.00 0.004.24.2 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.980.98

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State transition matrix:State transition matrix:

1.1. Educated guesses Educated guesses (hypotheses)(hypotheses)

2.2. Estimated from a series of land Estimated from a series of land use maps (e.g., from satellite use maps (e.g., from satellite imagery) to estimate transition imagery) to estimate transition frequencies.frequencies.

State transition matrix:State transition matrix:

1.1. Educated guesses Educated guesses (hypotheses)(hypotheses)

2.2. Estimated from a series of land Estimated from a series of land use maps (e.g., from satellite use maps (e.g., from satellite imagery) to estimate transition imagery) to estimate transition frequencies.frequencies.

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Transition probabilities:Transition probabilities:Source:Source: shrub, meadows shrub, meadowsTargets:Targets: • Pastures, grazingPastures, grazing• Agriculture (irrigated, rain fed)Agriculture (irrigated, rain fed)• Horticulture, orchards, wine, ….Horticulture, orchards, wine, ….• Urban fabric (housing)Urban fabric (housing)• Industrial/commercial areasIndustrial/commercial areas

Transition probabilities:Transition probabilities:Source:Source: shrub, meadows shrub, meadowsTargets:Targets: • Pastures, grazingPastures, grazing• Agriculture (irrigated, rain fed)Agriculture (irrigated, rain fed)• Horticulture, orchards, wine, ….Horticulture, orchards, wine, ….• Urban fabric (housing)Urban fabric (housing)• Industrial/commercial areasIndustrial/commercial areas

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A priori transition probabilities areCONTINGENT on

1. Global states (e.g., %S1)

2. Local States:

• Temporal (history, memory)

• Spatial (neighborhood)

A priori transition probabilities areCONTINGENT on

1. Global states (e.g., %S1)

2. Local States:

• Temporal (history, memory)

• Spatial (neighborhood)

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Global/local adjustments of the transition probabilities

in relative terms (p= 0.50; 10% increase p = 0.55

in absolute terms(p = 0.50 increase by 10% p = 0.60)

absolute(p = 0.50; set to 10% p = 0.10)

Global/local adjustments of the transition probabilities

in relative terms (p= 0.50; 10% increase p = 0.55

in absolute terms(p = 0.50 increase by 10% p = 0.60)

absolute(p = 0.50; set to 10% p = 0.10)

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Operators:Operators:1.1. FRACTION (in space: FRACTION (in space:

describes the neighborhood)describes the neighborhood)2.2. FREQUENCY (in time: FREQUENCY (in time:

describes the history)describes the history)3.3. LAST (in time: checks for LAST (in time: checks for

specific events)specific events)

Operators:Operators:1.1. FRACTION (in space: FRACTION (in space:

describes the neighborhood)describes the neighborhood)2.2. FREQUENCY (in time: FREQUENCY (in time:

describes the history)describes the history)3.3. LAST (in time: checks for LAST (in time: checks for

specific events)specific events)

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Example Concepts:Example Concepts:1.1. Cities are more likely to Cities are more likely to

expand than to start in the expand than to start in the middle of nowhere.middle of nowhere.

2.2. A dense city is likely to retain A dense city is likely to retain some last green areas like some last green areas like parks.parks.

Example Concepts:Example Concepts:1.1. Cities are more likely to Cities are more likely to

expand than to start in the expand than to start in the middle of nowhere.middle of nowhere.

2.2. A dense city is likely to retain A dense city is likely to retain some last green areas like some last green areas like parks.parks.

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Cities are more likely to expand than to start in Cities are more likely to expand than to start in the middle of nowhere:the middle of nowhere:

IF FRACTION(city,1) < 10%IF FRACTION(city,1) < 10%THEN p(*,city) DECREASES 90%THEN p(*,city) DECREASES 90%If in the immediate surrounding of an area If in the immediate surrounding of an area

(8 neighbors) there is not at least 1 cell (8 neighbors) there is not at least 1 cell that is already part of a city, the that is already part of a city, the probability of any source class to probability of any source class to become city is reduced to 1/10.become city is reduced to 1/10.

Cities are more likely to expand than to start in Cities are more likely to expand than to start in the middle of nowhere:the middle of nowhere:

IF FRACTION(city,1) < 10%IF FRACTION(city,1) < 10%THEN p(*,city) DECREASES 90%THEN p(*,city) DECREASES 90%If in the immediate surrounding of an area If in the immediate surrounding of an area

(8 neighbors) there is not at least 1 cell (8 neighbors) there is not at least 1 cell that is already part of a city, the that is already part of a city, the probability of any source class to probability of any source class to become city is reduced to 1/10.become city is reduced to 1/10.

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A dense city is likely to retain some A dense city is likely to retain some last green areas like parks.last green areas like parks.

IF FRACTION(city,2) > 95IF FRACTION(city,2) > 95THEN p(*,city) ABSOLUTE 0THEN p(*,city) ABSOLUTE 0If there is not at least one free green If there is not at least one free green

area in radius of 2 units (a 5by5 area in radius of 2 units (a 5by5 neighborhood) around a given plot, neighborhood) around a given plot, no transition to city is possible.no transition to city is possible.

A dense city is likely to retain some A dense city is likely to retain some last green areas like parks.last green areas like parks.

IF FRACTION(city,2) > 95IF FRACTION(city,2) > 95THEN p(*,city) ABSOLUTE 0THEN p(*,city) ABSOLUTE 0If there is not at least one free green If there is not at least one free green

area in radius of 2 units (a 5by5 area in radius of 2 units (a 5by5 neighborhood) around a given plot, neighborhood) around a given plot, no transition to city is possible.no transition to city is possible.

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Other attributes that can affect Other attributes that can affect potential land use:potential land use:

• Elevation, slopeElevation, slope

• Terrain, reliefTerrain, relief

• Soil, geologySoil, geology

• Climate (water)Climate (water)

• Infrastructure (transport, energy)Infrastructure (transport, energy)

Other attributes that can affect Other attributes that can affect potential land use:potential land use:

• Elevation, slopeElevation, slope

• Terrain, reliefTerrain, relief

• Soil, geologySoil, geology

• Climate (water)Climate (water)

• Infrastructure (transport, energy)Infrastructure (transport, energy)

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Other driving forces:Other driving forces:

• Population development, Population development, pressurespressures

• Migration (jobs, income, Migration (jobs, income, unemployment, conflicts)unemployment, conflicts)

• GRP, property pricesGRP, property prices

Other driving forces:Other driving forces:

• Population development, Population development, pressurespressures

• Migration (jobs, income, Migration (jobs, income, unemployment, conflicts)unemployment, conflicts)

• GRP, property pricesGRP, property prices

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Related analysis:Related analysis:• Employment opportunitiesEmployment opportunities

• GRP, revenues, taxes, incomeGRP, revenues, taxes, income

• Resource consumption (water, energy)Resource consumption (water, energy)

• Environmental impacts (waste streams)Environmental impacts (waste streams)

Based on land use specific processes or Based on land use specific processes or activity specific coefficientsactivity specific coefficients

Related analysis:Related analysis:• Employment opportunitiesEmployment opportunities

• GRP, revenues, taxes, incomeGRP, revenues, taxes, income

• Resource consumption (water, energy)Resource consumption (water, energy)

• Environmental impacts (waste streams)Environmental impacts (waste streams)

Based on land use specific processes or Based on land use specific processes or activity specific coefficientsactivity specific coefficients

© K.Fedra 20004

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Related analysis or models:Related analysis or models:• Traffic systemsTraffic systems• Forest fireForest fire• DesertificationDesertification• Avalanches, mud slidesAvalanches, mud slides• Development of a infrastructures:Development of a infrastructures:

– Road network, pipelines, district heatingRoad network, pipelines, district heating• Locational analysis Locational analysis (site suitability: schools, hospitals, (site suitability: schools, hospitals,

airports, supermarkets …..)airports, supermarkets …..)

Related analysis or models:Related analysis or models:• Traffic systemsTraffic systems• Forest fireForest fire• DesertificationDesertification• Avalanches, mud slidesAvalanches, mud slides• Development of a infrastructures:Development of a infrastructures:

– Road network, pipelines, district heatingRoad network, pipelines, district heating• Locational analysis Locational analysis (site suitability: schools, hospitals, (site suitability: schools, hospitals,

airports, supermarkets …..)airports, supermarkets …..)

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Assignment:Assignment:

Build a LUC scenario:Build a LUC scenario:

• Use the Lebanon exampleUse the Lebanon exampleOROR

• Use your own real or Use your own real or hypothetical initial conditionshypothetical initial conditions

Assignment:Assignment:

Build a LUC scenario:Build a LUC scenario:

• Use the Lebanon exampleUse the Lebanon exampleOROR

• Use your own real or Use your own real or hypothetical initial conditionshypothetical initial conditions

© K.Fedra 20004

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Assignment:Assignment:Develop a transition matrixDevelop a transition matrixDevelop a set of RULES, explain Develop a set of RULES, explain

the underlying ideas and the underlying ideas and principles !principles !

Suggest improvements to the Suggest improvements to the model.model.

Assignment:Assignment:Develop a transition matrixDevelop a transition matrixDevelop a set of RULES, explain Develop a set of RULES, explain

the underlying ideas and the underlying ideas and principles !principles !

Suggest improvements to the Suggest improvements to the model.model.

© K.Fedra 20004

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Assignment: LUCAssignment: LUC

Details and material on:Details and material on:http://80.120.147.30/LUC/

http://www.ess.co.at/SMART/luc.html

Assignment: LUCAssignment: LUC

Details and material on:Details and material on:http://80.120.147.30/LUC/


http://80.120.147.30/LUC/


http://80.120.147.30/LUC/


© k.fedra 20004 1 dynamic land use change modeling a simple spatial modeling application

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