Modelling Human-Environment Interactions: Theories and Tools Gilberto Câmara Licence: Creative Commons ̶̶̶̶ By Attribution ̶̶̶̶ Non Commercial ̶̶̶̶ Share Alike http://creativecommons.org/licenses/by-nc-sa/2.5/ Vespucci Summer School 2010
By the Year 2050… 9 billion people: 6 billion tons of GHG and 60 million tons of urban pollutants. Resource-hungry: We will withdraw 30% of available fresh water. Risky living: 80% urban areas, 25% near earthquake faults, 2% in coast lines less than 1 m above sea level.
The fundamental question of our time fonte: IGBP How is the Earths environment changing, and what are the consequences for human civilization?
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?
Impacts of global land change More vulnerable communities are those most at risk
Global Change Where are changes taking place? How much change is happening? Who is being impacted by the change? What is causing change? Human actions and global change photo: A. Reenberg photo: C. Nobre
Deforestation in Amazonia ~230 scenes Landsat/year
simplified representation of a process Model = entities + relations + attributes + rules What is a Model? Deforestation in Amazonia in 2020?
Computational models If (... ? ) then... Desforestation? Connect expertise from different fields Make the different conceptions explicit
Computational models Territory (Geography) Money (Economy) Culture (Antropology) Modelling (GIScience) Connect expertise from different fields Make the different conceptions explicit
Modelling and Public Policy System Ecology Economy Politics Scenarios Decision Maker Desired System State External Influences Policy Options
Slides from LANDSAT Aral Sea 197319872000 images: USGS Modelling Human-Environment Interactions How do we decide on the use of natural resources? Can we describe and predict changes resulting from human decisions? What computational tools are needed to model human- environment decision making?
Nature: Physical equations Describe processes Society: Decisions on how to Use Earth´s resources We need spatially explicit models to understand human-environment interactions
f ( I t+n ). FF f (I t )f (I t+1 )f (I t+2 ) Dynamic Spatial Models A dynamical spatial model is a computational representation of a real-world process where a location on the earths surface changes in response to variations on external and internal dynamics (Peter Burrough)
t p - 20 t p - 10 tptp Calibration t p + 10 Forecast Dynamic Spatial Models Source: Cláudia Almeida
Limits for Models 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
How do we decide on the use of natural resources? Loggers Competition for Space Soybeans Small-scale Farming Ranchers Source: Dan Nepstad (Woods Hole)
Human-enviromental systems [Ostrom, Science, 2005]
Institutional analysis 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
Statistics: Humans as clouds Establishes statistical relationship with variables that are related to the phenomena under study Basic hypothesis: stationary processes Example: CLUE Model (University of Wageningen) y=a 0 + a 1 x 1 + a 2 x 2 +... +a i x i +E Fonte: Verburg et al, Env. Man., Vol. 30, No. 3, pp. 391–405
Spatially-explicit LUCC models Explain past changes, through the identification of determining factors of land use change; Envision which changes will happen, and their intensity, location and time; Assess how choices in public policy can influence change, by building different scenarios considering different policy options.
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 (Université Louvain) 5% 10% 50% % of the cases What Drives Tropical Deforestation?
Driving factors of change (deforestation) source: Aguiar (2006)
Linear and spatial lag regression models where: Y is an (n x 1) vector of observations on a dependent variable taken at each of n locations, X is an (n x k) matrix of exogenous variables, is an (k x 1) vector of parameters (estimated regression coefficients), and is (n x 1) an vector of disturbances. W is the spatial weights matrix, the product WY expresses the spatial dependence on Y (neighbors), is the spatial autoregressive coefficient.
Statistics: Humans as clouds Statistical analysis of deforestation source: Aguiar (2006)
CLUE modeling framework 25 x 25 km 2 100 x 100 km 2
Scenario exploration: linking to process knowledge Cellular database construction Exploratory analysis and selection of subset of variables Construction of alternative models for each group/partition/ land-use Alternative CLUE runs 1997 to 2020 Comparison to real data and new frontiers process knowledge Porto Velho- Manaus BR 163 Cuiabá-Santarém São Felix/ Iriri ApuíHumaitá Boca do Acre Santarém Manaus- Boa Vista Aripuanã Scenario exploration
Agents as basis for complex systems Agent: flexible, interacting and autonomous An agent is any actor within an environment, any entity that can affect itself, the environment and other agents.
Agent-Based Modelling Goal Environment Representations Communication Action Perception Communication source: Nigel Gilbert
Agents: autonomy, flexibility, interaction Synchronization of fireflies
Bird Flocking No central authority: Each bird reacts to its neighbour Not possible to model the flock in a global manner. Need to necessary to simulate the INTERACTION between the individuals
Requirement #2 for human-environment models Models need to support both statistical relations (clouds) and agents (ants)
Question #3 for human-environment models What types of spatial relations exist in nature-society models?
Rondonia 19751986 Natural space is (usually) isotropic Societal space is mostly anisotropic
Which spatial objects are closer? Societal spaces are anisotropic Which cells are closer? [Aguiar et al., 2003]
Euclidean spaceOpen network Closed network D2 D1 Requirement #3 for human-environment models: express anisotropy explicitly [Aguiar et al., 2003]
Question #4 for human-environment models How do we combine independent multi-scale models with feedback?
Models: From Global to Local Athmosphere, ocean, chemistry climate model (200 x 200 km) Atmosphere only global climate model (50 x 50 km) Regional climate model (10 x 10 km) Hydrology, Vegetation Soil Topography (1 x 1 km) Regional land use change Socio-economic adaptation (e.g., 100 x 100 m)
National level - the main markets for Amazonia products (Northeast and São Paulo) and the roads infrastructure network; Regional level - for the whole Brazilian Amazonia, 4 million km2; Local level - for a hot-spot of deforestation in Central Amazonia, the Iriri region, in São Felix do Xingu, Pará State 25 x 25 km 2 1 x 1 km 2 Human-enviroment models should be multi-scale, multi-approach [Moreira et al., 2008]
Nested grids are not enough! Environmental Modeler [Engelen, White and Nijs, 2003] CLUE model [Veldkamp and Fresco, 1996] Multi-scale modelling: hierarchical relations need to be described
Requirement #4 for human-environment models: support multi-scale modelling using explicit relationships Express explicit spatial relationships between individual objects in different scales [Moreira et al., 2008] [Carneiro et al., 2008]
Question #5 for human-environment models Small Farmers Medium-Sized Farmers photos: Isabel Escada How can we express behavioural changes in human societies? When a small farmer becomes a medium-sized one, his behaviour changes
Old Settlements (more than 20 years) Recent Settlements (less than 4 years) Farms Settlements 10 to 20 anos Societal systems undergo phase transitions Isabel Escada, 2003 [Escada, 2003]
Requirement #5 for human-environment models: Capture phase transitions Newly implanted Deforesting Slowing down latency > 6 years Deforestation > 80% Small Farmers Iddle Year of creation Deforestation = 100% Deforesting Slowing down Iddle Year of creation Deforestation = 100% Deforestation > 60% Medium-Sized Farmers photos: Isabel Escada
TerraME: Computational environment for developing human-environment models Cell Spaces Support for cellular automata and agents Modular modelling tool [Carneiro, 2006]
Spatial structure in TerraME: Cell Spaces integrated with databases
TerraME´s approach: Modular components Describe spatial structure 1:32:00Mens. 1 1. 1:32:10Mens. 3 2. 1:38:07Mens. 2 3. 1:42:00Mens.4 4.... return value true 1. Get first pair 2. Execute the ACTION 3. Timer =EVENT 4. timeToHappen += period Describe temporal structure Newly implanted Deforesting Slowing down latency > 6 years Iddle Year of creation Deforestation = 100% Describe rules of behaviourDescribe spatial relations [Carneiro, 2006]
Spatial Relations in TerraME Spatial relations between entities in a nature-societal model are expressed by a generalized proximity matrix (GPM) [Moreira et al., 2008]
TerraME: multi-scale modelling using explicit relationships Generalized proximity matrices express explicit spatial relationships between individual objects in different scales up-scaling Scale 1 Scale 2 father children [Moreira et al., 2008] [Carneiro et al., 2008]
To Agent Cell a b a b c c Cell Agent From GPM: Relations between cells and agents [Andrade-Neto et al., 2008]
TerraME uses hybrid automata to represent phase transitions State A Flow Condition State B Flow Condition Jump condition A hybrid automaton is a formal model for a mixed discrete continuous system (Henzinger, 1996) Hybrid Automata = state machine + dynamical systems
Hybrid automata: simple land tenure model STATEFlow ConditionJump ConditionTransition SUBSISTENCEDeforest 10% of land/yearDeforest > 60%CATTLE Extensive cattle raisingLand exhaustionABANDONMENT Forest regrowthLand revisionRECLAIM Public repossessionLand registrationLAND REFORM Land distributionFarmer gets parcels SUBSISTENCE Deforest 20%/year Farmer gets parcel deforest>=60% Land exhaustion CATTLE Extensive cattle raising ABANDONMENT Regrowth RECLAIM Public repossession Land revision LAND REFORM redistribution Land registration
TerraME Software Architecture TerraLib TerraME Framework C++ Signal Processing librarys C++ Mathematical librarys C++ Statistical librarys TerraME Virtual Machine TerraME Compiler TerraME Language RondôniaModelSão Felix Model Amazon ModelHydro Model [Carneiro, 2006]
Lua and the Web Where is Lua? Inside Brazil Petrobras, the Brazilian Oil Company Embratel (the main telecommunication company in Brazil) many other companies Outside Brazil Lua is used in hundreds of projects, both commercial and academic CGILua still in restricted use until recently all documentation was in Portuguese TerraME Programming Language: Extension of LUA LUA is the language of choice for computer games [Ierusalimschy et al, 1996] source: the LUA team
Amazonia: multiscale analysis of land change and beef and milk market chains with TerraME Deforestation Forest Non-forest Clouds/no data INPE/PRODES 2003/2004: São Felix do Xingu
Forest Not Forest Deforest River Change 1997-2006: deforestation and cattle
Create pasture/ Deforest Speculator/ large/small bad land management money surplus Subsistence agriculture Diversify use Manage cattle Move towards the frontier Abandon/Sell the property Buy new land Settlement/ invaded land Sustainability path (alternative uses, technology) Sustainability path (technology) Agents example: small farmers in Amazonia
Create pasture/ plantation/ deforest Speculator/ large/small money surplus/bank loan Diversify use Buy new land Manage cattle/ plantation Buy calves from small Buy land from small farmers Agents example: large farmers in Amazonia
Forest Not Forest Deforest River Observed deforestation from 1997 to 2006
Local scale Regional scale CATTLE CHAIN MODEL Flows: goods, information, etc.. Connections: Agents LANDSCAPE DYNAMICS MODEL - Front - Medium - Rear INDIVIDUAL AGENTS Large and small farmers Local farmers Frontier Region SCENARIOS
Land use Change model Beef and milk market chain model Small farmers Medium and large farmers Land use Change model Small farmers Medium and large farmers Landscape metrics model Pasture degradation model Several workshops in 2007 to define model rules and variables Landscape model: different rules for two main types of actors
Landscape model: different rules of behavior at different partitions which also change in time FRENTE MEIO RETAGUARDA Forest Not Forest Deforest River FRONT MIDDLE BACK SÃO FÉLIX DO XINGU - 2006
Modeling results 97 to 2006 Observed 97 to 2006