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How can GIScience contribute to land change modelling

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Director, National Institute for Space Research, Brazil. GIScience 2006, Munster, Germany ... In: Auto-Carto 13 Vol. 5. ACSM/ASPRS, Seattle, WA (1997) 11-22. ... – PowerPoint PPT presentation

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Title: How can GIScience contribute to land change modelling


1
How can GIScience contribute to land change
modelling?
GIScience 2006, Munster, Germany
  • Gilberto Câmara
  • Director, National Institute for Space Research,
    Brazil

2
Motivation
  • Lets start from a real problem….
  • Building a road in the Amazon rain forest

3
Área de estudo ALAP BR 319 e entorno
new road
4
Can we avoid that this….
Source Carlos Nobre (INPE)
5
Fire...
….becomes this?
Source Carlos Nobre (INPE)
6
Amazonia Deforestation rate 1977-2004
7
BASELINE SCENARIO Hot spots of change (1997 a
2020)
ALAP BR 319
Estradas pavimentadas em 2010
Estradas não pavimentadas
Rios principais
8
GOVERNANCE SCENARIO Differences from baseline
scenario
Differences
Protection areas
ALAP BR 319
Estradas pavimentadas em 2010
Less
0.0
-0.50
Sustainable areas
Estradas não pavimentadas
More
0.0
0.10
Rios principais
9
(No Transcript)
10
  • Give us some new problems
  • (Dimitrios Papadias, SSTD 2005)

11
  • Give us some new problems

What about saving the planet?
12
(No Transcript)
13
The fundamental question
  • How is the Earths environment changing, and what
    are the consequences for human civilization?

Source NASA, IGBP
14
GIScience and change
  • We need a vision for extending GIScience to have
    a research agenda for modeling change

15
The Greek vision of spatial data
(x  y)2  x2  2xy  y2
Euclid
16
The Greek vision of spatial data
(x  y)2  x2  2xy  y2
Euclid
Egenhofer
spatial topology
17
The Greek vision of spatial data
Aristotle
categories - kathgoria
18
The Greek vision of spatial data
Aristotle
categories - kathgoria
Smith
SPAN ontologies
19
A challenge to GIScience
  • Time has come to move from Greece to the
    Renaissance!

20
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)

21
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.

22
The Renaissance vision
Kepler
23
The Renaissance vision
Kepler
Frank
24
The Renaissance vision
Galileo
25
The Renaissance vision
Galileo
Batty
26
Challenge How do people use space?
Loggers
Competition for Space
Source Dan Nepstad (Woods Hole)
27
Statistics Humans as clouds
ya0 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)

28
Statistics Humans as clouds
Statistical analysis of deforestation
29
The trouble with statistics
  • Extrapolation of current measured trends
  • How do we know if tommorow will be like today?
  • How do we incorporate feedbacks?

30
Cellular Automata Humans as Ants
  • Cellular Automata
  • Matrix,
  • Neighbourhood,
  • Set of discrete states,
  • Set of transition rules,
  • Discrete time.

CAs contain enough complexity to simulate
surprising and novel change as reflected in
emergent phenomena (Mike Batty)
31
Agents and CA Humans as ants
Identify different actors and try to model their
actions
32
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
  • Continuous variable
  • deforested
  • Two alternative neighborhood
  • relations
  • connection through roads
  • farm limits proximity

1997
1997
33
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

34
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 5of cases, not depicted here.
sourceGeist Lambin
35
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?

36
Some promising approaches
  • Hybrid automata
  • Flexible neighbourhoods
  • Nested cellular automata
  • Game theory

37
Hybrid Automata
  • Formalism developed by Tom Henzinger (UC
    Berkeley)
  • Combines discrete transition graphs with
    continous dynamical systems
  • Infinite-state transition system

Event
Control Mode A Flow Condition
Control Mode B Flow Condition
Jump condition
38
Flexible neighbourhoods
Consolidated area
Emergent area
39
Nested Cellular Automata
Environments can be nested
Multiscale modelling
Space can be modelled in different resolutions
40
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.

41
Game theory and mobility
Three strategies
A - ((10 200 0) B - ((50 200 0) C -
((100 200 0))
42
Game theory and mobility
  • What happens when players can move?

If a player loses too much, he might move to an
adjacent cell
43
Mobility breaks the Nash equilibrium!
44
The big challenge a theory of scale
45
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)
46
Multi-scale approach
47
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

48
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

49
Earth as a system
50
Global Land Project
  • What are the drivers and dynamics of variability
    and change in terrestrial human-environment
    systems?
  • How is the provision of environmental goods and
    services affected by changes in terrestrial
    human-environment systems?
  • What are the characteristics and dynamics of
    vulnerability in terrestrial human-environment
    systems?

51
The Renaissance vision
Principia
Newton
52
The Renaissance vision
Principia
Newton
Multiscale theory of space
Your picture here
????
53
Why is it so hard to model change?
Uncertainty on basic equations
Social and Economic Systems
Quantum Gravity
Particle Physics
Living Systems
Global Change
Hydrological Models
Chemical Reactions
Meteorology
Solar System Dynamics
Complexity of the phenomenon
source John Barrow (after David Ruelle)
54
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!!

55
thank you ! ??!
56
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
    TheoryA Theoretical Basis for GIS, International
    Conference COSIT '95, Semmering, Austria.
    Springer-Verlag, Berlin (1995) 1-15.

57
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.

58
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
    1990- LNCS 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.

59
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.

60
References
  • INPEs 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.
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