Transcript Introduction to Spatial Dynamical Modelling

Introduction to Enviromental
Modelling
Lecture 1 – Basic Concepts
Gilberto Câmara
Tiago Carneiro
Ana Paula Aguiar
Sérgio Costa
Pedro Andrade Neto
The fundamental question of our time
source: IGBP
How is the Earth’s environment
changing, and what are the
consequences for human
civilization?
Global Change
Where are changes taking place?
How much change is happening?
Who is being impacted by the change?
source: USGS
Slides from LANDSAT
Modelling Change: A Research Programme
Understanding how humans use space
Aral Sea
1973
1987
Predicting changes resulting from human actions
2000
Modeling the interaction between society and nature
Bolivia
1975
1992
2000
Modelling Complex Problems
source: Carneiro (2006)
If (... ? ) then ...
Deforestation?
Application of interdisciplinary knowledge to produce
a model.
What is Computational Modelling?

Design and implementation of computational
environments for modelling
 Requires a formal and stable description
 Implementation allows experimentation

Rôle of computer representation
 Bring together expertise in different field
 Make the different conceptions explicit
 Make sure these conceptions are represented in
the information system
Dynamic Spatial Models
f (It)
f (It+1)
F
f (It+2)
f ( It+n )
F
..
“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)
Dynamic Spatial Models
Scenario
tp - 20
tp - 10
tp
Calibration
source: Cláudia Almeida
Calibration
tp + 10
Modelling Human Actions: Two
Approaches

Models based on global factors
 Explanation based on causal models
 Human_actions

= f (factors,....)
Emergent models
 Local actions lead to global patterns
 Simple interactions between individuals
lead to
complex behaviour
 “The organism is intelligent, its parts are simpleminded”
Statistics: Humans as clouds
y=a0 + a1x1 + a2x2 + ... +aixi +E
Establishes statistical relationship with
variables that are related to the phenomena
under study
 Basic hypothesis: stationary processes
 Exemples: CLUE Model (University of
Wageningen)

Factors Affecting Deforestation
source: Aguiar (2006)
Category
Demographic
Technology
Variables
Population Density
Proportion of urban population
Proportion of migrant population (before 1991, from 1991 to 1996)
Number of tractors per number of farms
Percentage of farms with technical assistance
Agrarian strutucture Percentage of small, medium and large properties in terms of area
Percentage of small, medium and large properties in terms of number
Infra-structure
Distance to paved and non-paved roads
Distance to urban centers
Distance to ports
Economy
Distance to wood extraction poles
Distance to mining activities in operation (*)
Connection index to national markets
Percentage cover of protected areas (National Forests, Reserves,
Political
Presence of INCRA settlements
Number of families settled (*)
Environmental
Soils (classes of fertility, texture, slope)
Climatic (avarage precipitation, temperature*, relative umidity*)
Statistics: Humans as clouds
MODEL 7:
Variables
source: Aguiar (2006)
R² = .86
PORC3_AR
Description
Percentage of large farms, in terms of
area
LOG_DENS
Population density (log 10)
PRECIPIT
stb
p-level
0,27
0,00
0,38
0,00
-0,32
0,00
LOG_NR1
Avarege precipitation
Percentage 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
Land Change Model (1997-2015)
source: Aguiar (2006a)
Projected hot spots of deforestation 1997- 2015:
Federative States
Regionalizing the demand improves pressure on Central area, but
Central area regressions emphasizes proximity to ports and rivers,
due to historical process in the area, and not connectivity to the rest
of the country.
Roads
Percentage of change
in forest cover from 1997 to
2015:
0% ->
100%
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.

Segregation
Segregation 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 neighbours

 White cells change to black if there are X or more
black neighbours
 Black 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
Micro-level rules of the game
Stay if at least a
third of neighbors
are “kin”
< 1/3
Move to random location
otherwise
Schelling’s Model of Segregation
Tolerance values above 30%: formation of
ghettos
References

J. Zhang. Residential segregation in an allintegrationist world. Journal of Economic
Behaviour & Organization, v. 54 pp. 533550. 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