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

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. Source: Carlos Nobre (INPE) Can we avoid that this….

5. Fire... Source: Carlos Nobre (INPE) ….becomes this?

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 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 % mudança 1997 a 2020:

8. GOVERNANCE SCENARIO – Differences from baseline scenario ALAP BR 319 Estradas pavimentadas em 2010 Estradas não pavimentadas Rios principais 0.0-0.50 Less: 0.00.10 More: Differences: Protection areas Sustainable areas

10. “Give us some new problems” (Dimitrios Papadias, SSTD 2005)

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

13. The fundamental question How is the Earth’s 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 Euclid (x + y) 2 = x 2 + 2xy + y 2

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

17. The Greek vision of spatial data Aristotle categories - 

18. The Greek vision of spatial data Aristotle Smith categories -  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 Soybeans Small-scale Farming Ranchers Source: Dan Nepstad (Woods Hole)

27. 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=a 0 + a 1 x 1 + a 2 x 2 +... +a i x i +E

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

32. Agent model using Cellular Automata 1985 1997 Large farm environments: 2500 m resolution Continuous variable: % deforested Two alternative neighborhood relations: connection through roads farm limits proximity Small farms environments: 500 m resolution Categorical variable: deforested or forest One neighborhood relation: connection through roads

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. 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  5% 10% 50% % of the cases What Drives Tropical Deforestation?

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 Control Mode A Flow Condition Control Mode B Flow Condition Jump condition Event

38. Flexible neighbourhoods Consolidated areaEmergent area

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

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. B shootsB does not shoot A shoots(-10,-10)(+1,-1) A does not shoot(-1,+1)(0,0)

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

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 Newton Principia

52. The Renaissance vision Your picture here Newton ???? Principia Multiscale theory of space

53. Why is it so hard to model change? source: John Barrow (after David Ruelle) Complexity of the phenomenon Uncertainty on basic equations Solar System Dynamics Meteorology Chemical Reactions Hydrological Models Particle Physics Quantum Gravity Living Systems Global Change Social and Economic Systems

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!!

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 Theory—A 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 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.