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How can GIScience contribute to land change 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 a road in the Amazon rain forest. Área de estudo – ALAP BR 319 e entorno.

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How can GIScience contribute to land change modelling?

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

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

  3. Área de estudo – ALAP BR 319 e entorno ALAP BR 319 Estradas pavimentadas em 2010 Estradas não pavimentadas Rios principais Portos 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) % mudança 1997 a 2020: 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 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. “Give us some new problems” (Dimitrios Papadias, SSTD 2005)

  10. “Give us some new problems” What about saving the planet?

  11. The fundamental question • How is the Earth’s environment changing, and what are the consequences for human civilization? Source: NASA, IGBP

  12. GIScience and change • We need a vision for extending GIScience to have a research agenda for modeling change

  13. The Greek vision of spatial data (x + y)2 = x2 + 2xy + y2 Euclid

  14. The Greek vision of spatial data (x + y)2 = x2 + 2xy + y2 Euclid Egenhofer spatial topology

  15. The Greek vision of spatial data Aristotle categories - kathgoria

  16. The Greek vision of spatial data Aristotle categories - kathgoria Smith SPAN ontologies

  17. A challenge to GIScience Time has come to move from Greece to the Renaissance!

  18. 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)

  19. 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.

  20. The Renaissance vision Kepler

  21. The Renaissance vision Kepler Frank

  22. The Renaissance vision Galileo

  23. The Renaissance vision Galileo Batty

  24. Soybeans Ranchers Small-scale Farming Challenge: How do people use space? Loggers Competition for Space Source: Dan Nepstad (Woods Hole)

  25. 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

  26. Statistics: Humans as clouds Statistical analysis of deforestation

  27. The trouble with statistics • Extrapolation of current measured trends • How do we know if tommorow will be like today? • How do we incorporate feedbacks?

  28. Farms Settlements 10 to 20 anos Recent Settlements (less than 4 years) Old Settlements (more than 20 years) Source: Escada, 2003 Agents and CA: Humans as ants Identify different actors and try to model their actions

  29. 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

  30. 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

  31. 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

  32. 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?

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

  34. 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 Flow Condition Control Mode B Flow Condition

  35. Flexible neighbourhoods Consolidated area Emergent area

  36. U U U Nested Cellular Automata Environments can be nested Multiscale modelling Space can be modelled in different resolutions

  37. 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.

  38. Game theory and mobility Three strategies A - ((10%;; $200; 0) B - ((50%;; $200; 0) C - ((100%;; $200;; 0))

  39. Game theory and mobility • What happens when players can move? If a player loses too much, he might move to an adjacent cell

  40. Mobility breaks the Nash equilibrium!

  41. The big challenge: a theory of scale

  42. 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. Scale (Gibson et al. 2000)

  43. Multi-scale approach

  44. 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

  45. 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

  46. Earth as a system

  47. 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?

  48. The Renaissance vision Principia Newton

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