Transcript Vespucci Summer School 2010 Modelling Human-Environment Interactions: Theories and Tools Gilberto Câmara Licence: Creative Commons ̶̶̶̶ By Attribution ̶̶̶̶ Non Commercial ̶̶̶̶ Share Alike http://creativecommons.org/licenses/by-nc-sa/2.5/

Vespucci Summer School 2010
Modelling Human-Environment
Interactions: Theories and Tools
Gilberto Câmara
Licence: Creative Commons ̶̶̶̶ By Attribution ̶̶̶̶ Non Commercial ̶̶̶̶ Share Alike
http://creativecommons.org/licenses/by-nc-sa/2.5/
By the Year 2050…
9 billion people: 6 billion tons
of GHG and 60 million tons
of urban pollutants.
Resource-hungry: We will
withdraw 30% of available
fresh water.
Risky living: 80% urban areas,
25% near earthquake
faults, 2% in coast lines less
than 1 m above sea level.
The fundamental question of our time
How is the Earth’s
environment changing, and
what are the consequences for
human civilization?
fonte: IGBP
from Jackie McGlade (EEA)
Can we avoid that this….
Source: Carlos Nobre (INPE)
Fire...
….becomes this?
Source: Carlos Nobre (INPE)
source: Global Land Project Science Plan (IGBP)
Global Land Project
• What are the drivers and
dynamics of variability and
change in terrestrial humanenvironment systems?
• How is the provision of
environmental goods and
services affected by changes
in terrestrial humanenvironment systems?
• What are the characteristics
and dynamics of vulnerability
in terrestrial humanenvironment systems?
Impacts of global land change
More vulnerable communities are those most at risk
Human actions and global change
photo: C. Nobre
Global Change
Where are changes taking place?
How much change is happening?
Who is being impacted by the change?
What is causing change?
photo: A. Reenberg
~230 scenes
Landsat/year
Deforestation in Amazonia
What is a Model?
Deforestation in Amazonia in 2020?
simplified representation of a process
Model = entities + relations + attributes + rules
Computational models
Connect expertise from different fields
Make the different conceptions explicit
If (... ? ) then ...
Desforestation?
Computational models
Connect expertise from different fields
Make the different conceptions explicit
Territory
(Geography)
Money
(Economy)
Modelling
(GIScience)
Culture
(Antropology)
Modelling and Public Policy
External
Influences
System
Ecology
Economy
Politics
Scenarios
Policy
Options
Decision
Maker
Desired
System
State
Earth as a system
Physical Climate System
Climate
Change
Atmospheric Physics/Dynamics
Ocean Dynamics
Terrestrial
Energy/Moisture
Human
Activities
Global Moisture
Marine
Biogeochemistry
Terrestrial
Ecosystems
Tropospheric Chemistry
Biogeochemical Cycles
(from Earth System Science: An Overview, NASA, 1988)
Soil
CO2
Land
Use
CO2
Pollutants
images: USGS
Slides from LANDSAT
Modelling Human-Environment Interactions
How do we decide
on
the
use
of
natural
resources?
1973
1987
2000
Aral Sea
Can we describe and predict changes resulting from human
decisions?
What computational tools are needed to model humanenvironment decision making?
We need spatially explicit models to
understand human-environment interactions
Nature: Physical equations
Describe processes
Society: Decisions on how to
Use Earth´s resources
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” (Peter Burrough)
Dynamic Spatial Models
Forecast
tp - 20
tp - 10
tp
Calibration
Source: Cláudia Almeida
Calibration
tp + 10
Which is the better model?
Uncertainty on basic equations
Limits for Models
Social and Economic
Systems
Quantum
Gravity
Particle
Physics
Living
Systems
Global
Change
Chemical
Reactions
Hydrological
Models
Solar System Dynamics
Meteorology
Complexity of the phenomenon
source: John Barrow
(after David Ruelle)
How do we decide on the use of natural
resources?
Soybeans
Loggers
Competition for
Space
Small-scale Farming
Source: Dan Nepstad (Woods Hole)
Ranchers
Human-enviromental systems
[Ostrom, Science, 2005]
Types of goods
Source: E Ostrom (2005)
Institutional analysis
Identify different actors and try to model their actions
Farms
Settlements
10 to 20 anos
Recent
Settlements
(less than 4
years)
Source: Escada, 2003
Old
Settlements
(more than
20 years)
Institutional arrangements in Amazonia
Question #1 for human-environment models
What ontological kinds (data types) are required for
human-environment models?
Fields
Cells (objects)
Concepts for spatial dynamical models
Events and processes
Resilience
Concepts for spatial dynamical models
vulnerability
degradation
Concepts for spatial dynamical models
biodiversity
sustainability
and much more…
Human-environmental models need to describe complex
concepts (and store their attributes in a database)
Question #2 for human-environment models
What models are needed to describe human actions?
Clouds: statistical distributions
Clocks, clouds or ants?
Clocks: deterministic equations
Ants: emerging behaviour
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
Example: CLUE Model (University of Wageningen)
Fonte: Verburg et al, Env. Man., Vol. 30, No. 3, pp. 391–405
Spatially-explicit LUCC models



Explain past changes, through the identification of
determining factors of land use change;
Envision which changes will happen, and their intensity,
location and time;
Assess how choices in public policy can influence change, by
building different scenarios considering different policy
options.
What Drives Tropical Deforestation?
% of the cases
 5% 10% 50%
Underlying Factors
driving proximate causes
Causative interlinkages at
proximate/underlying levels
Internal drivers
*If less than 5%of cases,
not depicted here.
source:Geist &Lambin (Université Louvain)
Driving factors of change (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*)
Linear and spatial lag regression models
Y  Xβ  ε ,  ~ N( 0, )
2
Y  WY  Xβ  
where:
Y is an (n x 1) vector of observations
on a dependent variable taken at
each of n locations,
X is an (n x k) matrix of exogenous
variables,
 is an (k x 1) vector of parameters
(estimated regression
coefficients), and
 is (n x 1) an vector of disturbances.
W is the spatial weights matrix,
the product WY expresses the spatial
dependence on Y (neighbors),
 is the spatial autoregressive
coefficient.
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
Demand scenarios
40000
35000
30000
Rate (km2/year)
CLUE modeling framework
25000
Decreasing
20000
Baseline
Increasing
15000
10000
5000
Year
100 x 100 km2
100 x 100 km2
25 x 25 km2
20
20
20
18
20
16
20
14
20
12
20
10
20
08
20
06
20
04
20
02
20
00
19
98
19
96
19
94
19
92
19
90
19
88
0
Scenario exploration: linking to process knowledge
ManausBoa Vista Santarém
Cellular database
construction
Exploratory analysis
and
selection of
subset of variables
Porto Velho- São Felix/
Manaus
Iriri
Humaitá
Apuí BR 163
Boca do
Cuiabá-Santarém
Acre
Aripuanã
Scenario exploration
Construction of
alternative
models for each
group/partition/
land-use
Alternative
CLUE runs
1997 to 2020
Comparison to
real data and
new frontiers
process knowledge
Scenarios for deforestation in Amazonia (2020)
Agents as basis for complex systems
An agent is any actor within an environment, any entity
that can affect itself, the environment and other agents.
Agent: flexible, interacting and autonomous
Agent-Based Modelling
Representations
Goal
Communication
Communication
Action
Perception
Environment
source: Nigel Gilbert
Agents: autonomy, flexibility, interaction
Synchronization of fireflies
Bird Flocking
No central authority: Each bird reacts
to its neighbour
Not possible to model the flock in a
global manner. Need to necessary to
simulate the INTERACTION between
the individuals
Requirement #2 for human-environment models
Models need to support both statistical
relations (clouds) and agents (ants)
Question #3 for human-environment models
What types of spatial relations exist in
nature-society models?
Natural space is (usually) isotropic
Societal space is mostly anisotropic
1975
Rondonia
1986
Societal spaces are anisotropic
Which spatial objects are
closer?
Which cells are closer?
[Aguiar et al., 2003]
Requirement #3 for human-environment models:
express anisotropy explicitly
Euclidean space
Closed network
Open network
D1
D2
[Aguiar et al., 2003]
Question #4 for human-environment models
How do we combine
independent multi-scale
models with feedback?
Models: From Global to Local
Athmosphere, ocean, chemistry
climate model (200 x 200 km)
Atmosphere only global climate
model (50 x 50 km)
Regional climate model (10 x 10 km)
Hydrology, Vegetation
Soil Topography (1 x 1 km)
Regional land use change
Socio-economic adaptation (e.g.,
100 x 100 m)
Human-enviroment models should be multi-scale,
multi-approach
25 x 25 km2
1 x 1 km2
National level - the main markets for Amazonia products (Northeast and São
Paulo) and the roads infrastructure network;
Regional level - for the whole Brazilian Amazonia, 4 million km2;
Local level - for a hot-spot of deforestation in Central Amazonia, the Iriri region,
in São Felix do Xingu, Pará State
[Moreira et al., 2008]
Multi-scale modelling: hierarchical relations need
to be described
Environmental Modeler
[Engelen, White and Nijs, 2003]
CLUE model
[Veldkamp and Fresco, 1996]
Nested grids are not enough!
Requirement #4 for human-environment models:
support multi-scale modelling using explicit
relationships
Express explicit spatial relationships
between individual objects in
different scales
[Moreira et al., 2008]
[Carneiro et al., 2008]
Question #5 for human-environment models
photos: Isabel Escada
How can we express behavioural changes in
human societies?
Small Farmers
When a small
farmer becomes a
medium-sized one,
his behaviour
changes
Medium-Sized Farmers
Societal systems undergo phase transitions
Isabel Escada, 2003
Farms
Settlements
10 to 20 anos
Recent
Settlements
(less than 4
years)
Old
Settlements
(more than
20 years)
[Escada, 2003]
Requirement #5 for human-environment models:
Capture phase transitions
latency
> 6 years
Deforesting
Newly implanted
Small Farmers
Deforestation >
80%
Year of
creation
Slowing down
Iddle
Deforestation =
100%
photos: Isabel Escada
Deforesting
Deforestation >
60%
Year of
creation
Slowing down
Iddle
Deforestation =
100%
Medium-Sized
Farmers
TerraME: Computational environment for
developing human-environment models
Cell Spaces
Support for cellular
automata and agents
Modular modelling tool
[Carneiro, 2006]
Spatial structure in TerraME: Cell Spaces
integrated with databases
TerraME´s approach: Modular components
[Carneiro, 2006]
1. Get first pair
2. Execute the ACTION
3. Timer =EVENT
1.
1:32:00
Mens. 1
2.
1:32:10
Mens. 3
3.
1:38:07
Mens. 2
4.
1:42:00
Mens.4
...
return value
true
4. timeToHappen += period
Describe spatial structure
latency
> 6 years
Describe temporal structure
Deforesting
Newly implanted
Year of
creation
Iddle
Slowing down
Deforestation =
100%
Describe rules of behaviour
Describe spatial relations
Spatial Relations in TerraME
[Moreira et al., 2008]
Spatial relations between entities
in a nature-societal model are
expressed by a generalized
proximity matrix (GPM)
 w11
w
W   21
 w31

 w41
w12 w13 w14 
w22 w23 w24 
w32 w33 w34 

w42 w43 w44 
TerraME: multi-scale modelling using explicit
relationships
[Moreira et al., 2008]
[Carneiro et al., 2008]
Scale 1
father
up-scaling
children
Scale 2
Generalized proximity matrices
express explicit spatial relationships
between individual objects in
different scales
 w11
w
W   21
 w31

 w41
w12 w13 w14 
w22 w23 w24 
w32 w33 w34 

w42 w43 w44 
GPM: Relations between cells and agents
a
From
Cell
Agent
Agent
To
Cell
a
[Andrade-Neto et
al., 2008]
b
c
b
c
TerraME uses hybrid automata to represent phase
transitions
A hybrid automaton is a formal model for a mixed
discrete continuous system (Henzinger, 1996)
Hybrid Automata = state machine + dynamical systems
State A
Flow
Condition
State B
Jump
condition
Flow
Condition
Hybrid automata: simple land tenure model
Farmer gets parcel
SUBSISTENCE deforest>=60%
CATTLE
Deforest 20%/year
Extensive cattle raising
LAND REFORM
redistribution
Land exhaustion
Land revision ABANDONMENT
RECLAIM
Land registration
Public repossession
Regrowth
STATE
Flow Condition
Jump Condition
Transition
SUBSISTENCE
Deforest 10% of land/year
Deforest > 60%
CATTLE
CATTLE
Extensive cattle raising
Land exhaustion
ABANDONMENT
ABANDONMENT
Forest regrowth
Land revision
RECLAIM
RECLAIM
Public repossession
Land registration
LAND REFORM
LAND REFORM
Land distribution
Farmer gets
parcels
SUBSISTENCE
TerraME Software Architecture
RondôniaModel
São Felix Model
Amazon Model
Hydro Model
TerraME Language
TerraME Compiler
TerraME Virtual Machine
TerraLib
TerraME Framework
C++ Signal
Processing
librarys
C++
Mathematical
librarys
C++
Statistical
librarys
TerraLib
[Carneiro, 2006]
Where is Lua?

Inside Brazil
Petrobras, the Brazilian Oil Company
 Embratel
(the main telecommunication
company in Brazil)
TerraME
Programming
Language: Extension
of LUA
 many other companies

LUA
the language
of choice for computer games
 is
Outside
Brazil


Lua is used in hundreds of projects, both commercial and academic
CGILua still in restricted use

until recently all documentation was in Portuguese
Lua and the Web
source: the LUA team
[Ierusalimschy et al, 1996]
TerraME programming environment
TerraME INTERPRETER
• model syntax semantic checking
• model execution
TerraView
• data acquisition
• data visualization
• data management
• data analysis
LUA interpreter
TerraME framework
data
model
model
TerraME/LUA interface
data
Eclipse & LUA plugin
• model description
• model highlight syntax
MODEL DATA
Model
source code
TerraLib
database
[Carneiro, 2006]
Amazonia: multiscale analysis of land change and
beef and milk market chains with TerraME
São Felix do Xingu
INPE/PRODES 2003/2004:
Deforestation
Forest
Non-forest
Clouds/no data
Change 1997-2006: deforestation and cattle
Beef and milk
market chain model
Land use
Change model
Small
farmers
agents
Medium
and large
farmers
agents
Forest
River
Deforest
Not Forest
Agents example: small farmers in Amazonia
Settlement/
invaded land
Sustainability path
(alternative uses, technology)
Diversify use
money surplus
Subsistence
agriculture
Create pasture/
Deforest
Manage cattle
bad land
management
Move towards
the frontier
Sustainability
path (technology)
Abandon/Sell
the property
Buy new
land
Speculator/
large/small
Agents example: large farmers in Amazonia
Diversify use
money surplus/bank loan
Buy land
from small
farmers
Create pasture/
plantation/
deforest
Manage cattle/
plantation
Buy new
land
Buy calves
from small
Speculator/
large/small
Observed deforestation from 1997 to 2006
Forest
River
Deforest
Not Forest
Regional scale
Frontier
INDIVIDUAL AGENTS
Large and small farmers
Local scale
SCENARIOS
LANDSCAPE DYNAMICS MODEL
- Front
- Medium
- Rear
Local farmers
Region
CATTLE CHAIN MODEL
Flows: goods, information, etc..
Connections: Agents
Landscape model: different rules for two main
types of actors
Beef and milk
market chain model
Land use
Change model
Small
farmers
Medium
and large
farmers
Landscape
metrics
model
Pasture
degradation
model
Several workshops in 2007 to define model rules and
variables
Landscape model: different rules of behavior at different
partitions which also change in time
SÃO FÉLIX DO XINGU - 2006
FRONT
FRENTE
MIDDLE
MEIO
BACK
RETAGUARDA
Forest
River
Deforest
Not Forest
Modeling results
97 to 2006
Observed
97 to 2006