Transcript How can GIScience contribute to LUCC modelling?

GIScience 2006, Munster, Germany
How can GIScience
contribute to land change
modelling?
Gilberto Câmara
Director, National Institute for Space
Research, Brazil
Motivation
 Let’s
start from a real problem….
 Building
forest
a road in the Amazon rain
Área de estudo – ALAP BR 319 e entorno
new road
ALAP BR 319
Estradas pavimentadas em 2010
Estradas não pavimentadas
Rios principais
Portos
Can we avoid that this….
Source: Carlos Nobre (INPE)
Fire...
….becomes this?
Source: Carlos Nobre (INPE)
Amazonia Deforestation rate
1977-2004
35000
Annual deforestation rate
30000
20000
15000
10000
5000
Period
4(
*)
03
/0
3(
*)
fe
v
02
/0
1/
1
00
/0
0
99
/0
9
98
/9
8
97
/9
7
96
/9
6
95
/9
5
94
/9
**
4
2
92
/9
91
/9
1
90
/9
0
89
/9
9
88
/8
8
*
0
77
/8
km2/year
25000
?
BASELINE SCENARIO – Hot spots of change (1997 a 2020)
% mudança 1997 a 2020:
ALAP BR 319
Estradas pavimentadas em 2010
Estradas não pavimentadas
Rios principais
0.0 – 0.1
0.1 – 0.2
0.2 – 0.3
0.3 – 0.4
0.4 – 0.5
0.5 – 0.6
0.6 – 0.7
0.7 – 0.8
0.8 – 0.9
0.9 – 1.0
GOVERNANCE SCENARIO – Differences from baseline
scenario
ALAP BR 319
Estradas pavimentadas em 2010
Estradas não pavimentadas
Rios principais
Protection areas
Sustainable areas
Differences:
Less:
More:
0.0
-0.50
0.0
0.10
“Give us some new problems”
(Dimitrios Papadias, SSTD 2005)
“Give us some new problems”
What about saving the planet?
The fundamental question

How is the Earth’s environment changing,
and what are the consequences for human
civilization?
Source: NASA,
GIScience and change
 We
need a vision for extending
GIScience to have a research agenda
for modeling change
The Greek vision of spatial data
Euclid
(x + y)2 = x2 + 2xy + y2
The Greek vision of spatial data
Euclid
Egenhofer
(x + y)2 = x2 + 2xy + y2
spatial topology
The Greek vision of spatial data
Aristotle
categories - kathgoria
The Greek vision of spatial data
Aristotle
categories - kathgoria
Smith
SPAN ontologies
A challenge to GIScience
Time has come to move from Greece
to the Renaissance!
The Renaissance Vision

“No human inquiry can be called true science
unless it proceeds through mathematical
demonstrations” (Leonardo da Vinci)

“Mathematical principles are the alphabet in
which God wrote the world” (Galileo)
The Renaissance vision for space

Rules and laws that enable:

Understanding how humans use space;

Predicting changes resulting from human
actions;

Modeling the interaction between humans and
the environment.
The Renaissance vision
Kepler
The Renaissance vision
Kepler
Frank
The Renaissance vision
Galileo
The Renaissance vision
Galileo
Batty
Challenge: How do people use space?
Soybeans
Loggers
Competition for
Space
Small-scale Farming
Source: Dan Nepstad (Woods Hole)
Ranchers
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)
Statistics: Humans as clouds
MODEL 7:
Variables
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
The trouble with statistics

Extrapolation of current measured trends

How do we know if tommorow will be like today?

How do we incorporate feedbacks?
Agents and CA: Humans as ants
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)
Agent model using Cellular Automata
1985
Small farms environments:
500 m resolution
Categorical variable:
deforested or forest
One neighborhood relation:
•connection through roads
Large farm environments:
2500 m resolution
1997
Continuous variable:
% deforested
Two alternative neighborhood
relations:
•connection through roads
• farm limits proximity
1997
The trouble with agents

Many agent models focus on proximate causes
 directly
linked to land use changes
 (in the case of deforestation, soil type, distance to
roads, for instance)

What about the underlying driving forces?
 Remote
in space and time
 Operate at higher hierarchical levels
 Macro-economic changes and policy changes
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
Humans are not clouds nor ants!

“Third culture”
 Modelling
of physical phenomena
 Understanding of human dimensions

How to model human actions?
 What
makes people do certain things?
 Why do people compete or cooperate?
 What are the causative factors of human actions?
Some promising approaches

Hybrid automata

Flexible neighbourhoods

Nested cellular automata

Game theory
Hybrid Automata



Formalism developed by Tom Henzinger
(UC Berkeley)
Combines discrete transition graphs with
continous dynamical systems
Infinite-state transition system
Event
Jump condition
Control Mode A
Control Mode B
Flow Condition
Flow Condition
Flexible neighbourhoods
Consolidated area
Emergent area
Nested Cellular Automata
U
U
U
Environments can be nested
Multiscale modelling
Space can be modelled in different resolutions
Game theory and mobility




Two players get in a strive can choose shoot or
not shoot their firearms.
If none of them shoots, nothing happens.
If only one shoots, the other player runs away,
and then the winner receives $1.
If both decide to shoot, each group pays $10
due to medical cares.
B shoots
B does not shoot
A shoots
(-10,-10)
(+1,-1)
A does not shoot
(-1,+1)
(0,0)
Game theory and mobility
Three strategies
A - ((10%;; $200; 0)
B - ((50%;; $200; 0)
C - ((100%;; $200;; 0))
Game theory and mobility

What happens when players can move?
If a player loses too
much, he might move to
an adjacent cell
Mobility breaks the Nash equilibrium!
The big challenge: a theory of scale
Scale
Scale is a generic concept that includes the
spatial, temporal, or analytical dimensions used
to measure any phenomenon.
Extent refers to the magnitude of measurement.
Resolution refers to the granularity used in the
measures.
(Gibson et al. 2000)
Multi-scale approach
The trouble with current theories of scale


Conservation of “energy”: national demand is
allocated at local level
No feedbacks are possible: people are guided
from the above
The search for a new theory of scale



Non-conservative: feedbacks are possible
Linking climate change and land change
Future of cities and landscape integrate to the
earth system
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
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?
The Renaissance vision
Newton
Principia
The Renaissance vision
Newton
????
Your
picture
here
Principia
Multiscale theory of space
Uncertainty on basic equations
Why is it so hard to model change?
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)
Towards a research agenda

Moving GIScience from Greece to the
Renaissance….

GIScience – Formal and mathematical tools for
dealing with space

GIScience tools are crucial for supporting earth
system science

We have a lot of challenges ahead of us!!
References

Max Egenhofer
 Egenhofer,
M., Franzosa, R.: Point-Set Topological
Spatial Relations. International Journal of Geographical
Information Systems, 5 (1991) 161-174.
 Egenhofer, M., Franzosa, R.: On the Equivalence of
Topological Relations. International Journal of
Geographical Information Systems, 9 (1995) 133-152.
 Egenhofer, M., Mark, D.: Naive Geography. In: Frank,
A., Kuhn, W.(ed.): Spatial Information Theory—A
Theoretical Basis for GIS, International Conference
COSIT '95, Semmering, Austria. Springer-Verlag, Berlin
(1995) 1-15.
References

Barry Smith
 Smith,
B., Mark, D.: Ontology and Geographic Kinds.
In: Puecker, T., Chrisman, N. (ed.): International
Symposium on Spatial Data Handling. Vancouver,
Canada (1998) 308-320.
 Smith, B., Varzi, A.: Fiat and Bona Fide Boundaries.
Philosophy and Phenomenological Research, 60
(2000).
 Grenon, P., Smith, B.: SNAP and SPAN: Towards
Dynamic Spatial Ontology. Spatial Cognition &
Computation, 4 (2003) 69-104.
References

Andrew Frank
 Frank, A.:
One Step up the Abstraction Ladder:
Combining Algebras - From Functional Pieces to a
Whole. In: Freksa, C., Mark, D. (ed.): COSIT 1990LNCS 1661. Springer-Verlag (1999) 95-108.
 Frank, A.: Higher order functions necessary for spatial
theory development. In: Auto-Carto 13 Vol. 5.
ACSM/ASPRS, Seattle, WA (1997) 11-22.
 Frank, A.: Ontology for Spatio-temporal Databases.
In: Koubarakis, M., Sellis, T.(ed.): Spatio-Temporal
Databases: The Chorochronos Approach. Springer,
Berlin (2003) 9-78.
References

Mike Batty
 Batty,
M. Cities and Complexity: Understanding Cities
Through Cellular Automata, Agent-Based Models, and
Fractals. The MIT Press, Cambridge, MA, 2005.
 Batty,
M.; Torrens, P. M. “Modelling and Prediction in a
Complex World”. Futures, 37 (7), 745-766, 2005.
 Batty,
M. Xie, Y. Possible Urban Automata.
Environment and Planning B, 24, 175-192, 1996.
References

INPE’s recent work (see www.dpi.inpe.br/gilberto)

Almeida, C.M., Monteiro, A.M.V., Camara, G., Soares-Filho,
B.S., Cerqueira, G.C., Pennachin, C.L., Batty, M.: “Empiricism
and Stochastics in Cellular Automaton Modeling of Urban Land
Use Dynamics” Computers, Environment and Urban Systems,
27 (2003) 481-509.

Ana Paula Dutra de Aguiar, “Modeling Land Use Change in the
Brazilian Amazon: Exploring Intra-Regional Heterogeneity”.
PhD in Remote Sensing, INPE, 2006.

Tiago Garcia de Senna Carneiro, “"Nested-CA: A Foundation
for Multiscale Modelling of Land Use and Land Cover Change”.
PhD in Computer Science, INPE, 2006.