Introduction to Enviromental ModellingLecture 1 – Basic Concepts

Gilberto CâmaraTiago Carneiro Ana Paula AguiarSérgio CostaPedro Andrade Neto

source: IGBP

How is the Earth’s environment changing, and what are the consequences for human civilization?

The fundamental question of our time

Global Change

Where are changes taking place? How much change is happening? Who is being impacted by the change?

Slides from LANDSAT

Aral Sea

Bolivia 1975 1992 2000

1973 1987 2000

source: USGS

Modelling Change: A Research Programme

Understanding how humans use space

Predicting changes resulting from human actions

Modeling the interaction between society and nature

Modelling Complex Problems

Application of interdisciplinary knowledge to produce a model.

If (... ? ) then ...

Deforestation?

source: Carneiro (2006)

What is Computational Modelling?

Design and implementation of computational environments for modellingRequires a formal and stable description Implementation allows experimentation

Rôle of computer representation Bring together expertise in different fieldMake the different conceptions explicitMake sure these conceptions are represented in the

information system

f ( It+n )

. . FF

f (It) f (It+1) f (It+2)

Dynamic Spatial Models

“A dynamical spatial model is a computational representation of a real-world process where a location on the earth’s surface changes in response to variations on external and internal dynamics on the landscape” (Peter Burrough)

tp - 20 tp - 10

tp

Calibration Calibration tp + 10

ScenarioScenario

Dynamic Spatial Models

source: Cláudia Almeida

Modelling Human Actions: Two Approaches Models based on global factors

Explanation based on causal modelsHuman_actions = f (factors,....)

Emergent modelsLocal actions lead to global patternsSimple interactions between individuals lead to

complex behaviour“The organism is intelligent, its parts are simple-

minded”

Statistics: Humans as clouds

Establishes statistical relationship with variables that are related to the phenomena under study

Basic hypothesis: stationary processes Exemples: CLUE Model (University of

Wageningen)

y=a0 + a1x1 + a2x2 + ... +aixi +E

Factors Affecting DeforestationCategory Variables

Demographic Population DensityProportion of urban populationProportion of migrant population (before 1991, from 1991 to 1996)

Technology Number of tractors per number of farmsPercentage of farms with technical assistance

Agrarian strutucture Percentage of small, medium and large properties in terms of areaPercentage of small, medium and large properties in terms of number

Infra-structure Distance to paved and non-paved roadsDistance to urban centersDistance to ports

Economy Distance to wood extraction polesDistance to mining activities in operation (*)Connection index to national markets

Political Percentage cover of protected areas (National Forests, Reserves, Presence of INCRA settlementsNumber of families settled (*)

Environmental Soils (classes of fertility, texture, slope)Climatic (avarage precipitation, temperature*, relative umidity*)

source: Aguiar (2006)

Statistics: Humans as cloudsMODEL 7: R² = .86

Variables Description stb p-level

PORC3_ARPercentage of large farms, in terms of area 0,27 0,00

LOG_DENS Population density (log 10) 0,38 0,00

PRECIPIT Avarege precipitation -0,32 0,00

LOG_NR1Percentage of small farms, in terms of number (log 10) 0,29 0,00

DIST_EST Distance to roads -0,10 0,00

LOG2_FER Percentage of medium fertility soil (log 10) -0,06 0,01

PORC1_UC Percantage of Indigenous land -0,06 0,01

Statistical analysis of deforestation

source: Aguiar (2006)

Land Change Model (1997-2015)

0% -> 100%

Federative States

Roads

Projected hot spots of deforestation 1997- 2015:

Percentage of changein forest cover from 1997 to 2015:

Regionalizing the demand improves pressure on Central area, butCentral area regressions emphasizes proximity to ports and rivers,due to historical process in the area, and not connectivity to the restof the country.

source: Aguiar (2006a)

What are complex adaptive systems?

Systems composed of many interacting parts that evolve and adapt over time.

Organized behavior emerges from the simultaneous interactions of parts without any global plan.

SegregationSegregation is an outcome of individual choices

But high levels of segregation mean that people are prejudiced?

Schelling Model for Segregation

Start with a CA with “white” and “black” cells (random)

The new cell state is the state of the majority of the cell’s Moore neighboursWhite cells change to black if there are X or more

black neighboursBlack cells change to white if there are X or more

white neighbours How long will it take for a stable state to

occur?

Schelling’s Model of Segregation

Schelling (1971) demonstrates a theory to explain the persistence of racial segregation in an environment of growing tolerance

If individuals will tolerate racial diversity, but will not tolerate being in a minority in their locality, segregation will still be the equilibrium situation

Schelling’s Model of Segregation

< 1/3

Micro-level rules of the game

Stay if at least a third of neighbors are “kin”

Move to random location otherwise

Tolerance values above 30%: formation of ghettos

http://ccl.northwestern.edu/netlogo/models/Segregation

Schelling’s Model of Segregation

References

J. Zhang. Residential segregation in an all-integrationist world. Journal of Economic Behaviour & Organization, v. 54 pp. 533-550. 2004

T. C. Shelling. Micromotives and Macrobehavior. Norton, New York. 1978

Zhang: Residential segregation in an all-integrationist world

Some studies show that most people prefer to live in a non-segregated society. Why there is so much segregation?

Satisfaction

Satisfaction

Agents moving

Agents moving

Agents moving

Simulation