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 An n u a l d e fo r e s ta tio n r a te 30000 20000 2 k m /y e a r 25000 15000 10000 5000 P e r io d *) 4( /0 03 /0 3( *) fe v 02 1/ 1 00 /0 0 99 /0 9 98 /9 8 97 /9 7 96 /9 6 95 /9 5 /9 4 /9 94 ** 2 92 91 /9 1 90 /9 0 /9 89 /8 88 77 /8 8 * 9 0 ? 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 P h y s ic a l C lim a te S y s te m C lim a te Change A tm ospheric P hysics/D ynam ics O cean D yn am ics T errestrial E n erg y/M o istu re H u m an A ctivities G lo b al M o istu re M arin e B io g eo ch em istry T errestrial E co system s T ro p o s p h e ric C h e m is try B io g e o c h e m ic a l C y c le s (fro m E art h Syst em S cie nce : A n O ve rvie w , N A S A , 1 98 8 ) S o il C O2 La nd Use C O2 P olluta nts 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.