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Chapter 8

Chapter 8. Geocomputation Part A: Cellular Automata (CA) & Agent-based modelling (ABM). Geocomputation. “the art and science of solving complex spatial problems with computers” www.geocomputation.org Key new areas of geocomputation: Presentation 8A: Geosimulation (CA and ABM)

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Chapter 8

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  1. Chapter 8 Geocomputation Part A: Cellular Automata (CA) & Agent-based modelling (ABM) www.spatialanalysisonline.com

  2. Geocomputation “the art and science of solving complex spatial problems with computers” www.geocomputation.org Key new areas of geocomputation: Presentation 8A: Geosimulation (CA and ABM) Presentation 8B: Artificial Neural Networks (ANNs); & Evolutionary computing (EC) www.spatialanalysisonline.com

  3. Geocomputation Many other, well-established areas: • Automated zoning/re-districting (e.g. AZP) • Cluster hunting (e.g. GAM/K) • Interactive data mining tools (e.g. brushing and linking, cross-tabbed attribute mapping) • Visualisation tools (e.g. 3D and 4D visualisation, immersive systems… some also very new!) • Advanced raster processing (e.g. ACS/distance transforms, visibility analysis, image processing etc.) • Heuristic and metaheuristic spatial optimisation, …. and more! www.spatialanalysisonline.com

  4. Geocomputation: Geosimulation For the purposes of this discussion: Geosimulation includes • Cellular automata (CA) • Agent-based modelling (ABM) Geosimulation is particularly concerned with • Researching processes • Identifying and understanding emergent behaviours and outcomes • Spatio-temporal modelling www.spatialanalysisonline.com

  5. Geocomputation: ANNs In the next presentation on geocomputation: ANNs discussed include • Multi-level perceptrons (MLPs) • Radial basis function neural networks (RBFNNs) • Self organising feature maps (SOFMs) ANNs are particularly concerned with • Function approximation and interpolation • Image analysis and classification • Spatial interaction modelling www.spatialanalysisonline.com

  6. Geocomputation: Evolutionary computing In the next presentation on geocomputation: EC elements discussed include • Genetic algorithms (GAs) • Genetic programming (GP) EC is particularly concerned with • Complex problem solving using GAs • Model design using GP methods www.spatialanalysisonline.com

  7. Cellular automata (CA) • CA are computer based simulations that use a static cell framework or lattice as the environment (model of space) • Each cells has a well-defined state at every specific discrete point in time • Cell states may change over time according to state transition rules • Transition rules that are applied to cells depend upon their neighbourhoods (i.e. the states of adjacent cells typically) www.spatialanalysisonline.com

  8. Cellular automata • State variables • typically binary (e.g. alive/dead), but can be more complex • may have fixed (captured) states • Spatial framework • typically a regular lattice, but could be irregular • boundary issues and edge wrapping options • Neighbourhood structure • Typically Moore (8-way) or von Neumann (4-way) • Typically lag=1 but lag=2 .. and alternatives are possible • Transition rules • Typically deterministic but may be more complex • Time treated as discrete steps and all operations are synchronous (parallel not sequential changes) www.spatialanalysisonline.com

  9. Cellular automata Neighbourhood structure • Typically Moore (8-way) or von Neumann (4-way) • Typically lag=1 but lag=2 .. and alternatives are possible www.spatialanalysisonline.com

  10. Cellular automata Example 1 – Game of life • State variables: cells contain a 1 or a 0 (alive or dead) • Spatial framework: operates over a rectangular lattice (with square cells) • Neighbourhood structure: 4 adjacent (rook’s move) cells • State transition rules: time tntn+1 • Survival: if state=1 and in neighbourhood 2 or 3 cells have state=1 then state  1 else state  0 • Reproduction: if state=0 but state=3 or 4 in neighbouring cells then state  1 • Death (loneliness or overcrowding): if state=1 but state<>2 or 3 in neighbourhood then state  0 www.spatialanalysisonline.com

  11. Cellular automata Life (ABM framework): Click image to run model (Internet access required) t0 35% cell occupancy Randomly assigned tn – evolved pattern (still evolving – to density 4%) www.spatialanalysisonline.com

  12. Cellular automata Example 2 – Heatbugs • State variables: • Cells may be occupied by bugs or not • Cells have an ambient temperature value 0 • Bugs have an ideal heat (min and max rates settable) – i.e. a state of ‘happiness’ • State transition rules: time tntn+1 • Bugs can move, but only to an adjacent cell that does not have a bug on it • Bugs move if they are ‘unhappy’ – too hot or too cold (if they can move to a better adjacent cell) • Bugs emit heat (min and max rates settable) • Heat diffuses slowly through the grid and some is lost to ‘evaporation’ www.spatialanalysisonline.com

  13. Cellular automata Heatbugs (ABM framework): Click image to run model (Internet access required) www.spatialanalysisonline.com

  14. Cellular automata • Example geospatial modelling applications: • Bushfires • Deforestation • Earthquakes • Rainforest dynamics • Urban systems • But.. • Not very flexible • Difficult to adequately model mobile entities (e.g. pedestrians, vehicles)… interest in ABM www.spatialanalysisonline.com

  15. Agent-based modelling • Dynamic systems of multiple interacting agents • Agents are complex ‘individuals’ with various primary characteristics, e.g. • Autonomy, Mobility, Reactive or pro-active behaviour, Vision, Communications capabilities, Learning capabilities • Operate within a model or simulation environment • Time treated synchronously or asynchronously • CA can be modelling using ABM, but reverse may be difficult • Bottom-up rather than top-down modelling www.spatialanalysisonline.com

  16. Agent-based modelling • Sample applications: • Archaeological reconstruction • Biological models of infectious diseases • Modelling economic processes • Modelling political processes • Traffic simulations • Analysis of social networks • Pedestrian modelling (crowds behaviour, evacuation modelling etc.) … www.spatialanalysisonline.com

  17. Agent-based modelling • Example 1: Schelling segregation model Actually a CA model implemented here in an ABM framework. Agents represent people; agent interactions model a social process • Spatial framework: Cell based • State variables: grey – cell unoccupied; red – occupied by red group; black – occupied by black group • Neighbourhood structure (Moore) • State transition rules: • If proportion of neighbours of the same colour x% then stay where you are, else • If proportion of neighbours of the same colour <x% then move to an unoccupied cell or leave entirely www.spatialanalysisonline.com

  18. Agent-based modelling Schelling (ABM framework): Click image to run model (Internet access required) www.spatialanalysisonline.com

  19. Agent-based modelling • Example 2: Pedestrian movement • Realistic spatial framework • Multiple passengers arriving and departing • Multiple targets – ticket machines, ticket booths, subway platforms, mainline platforms, shop, exits … • Free movement with obstacle avoidance www.spatialanalysisonline.com

  20. Agent-based modelling Pedestrian movement: Click image to run model (Internet access required) www.spatialanalysisonline.com

  21. Agent-based modelling • Advantages of ABM • Captures emergent phenomena • Interactions can be complicated, non-linear, discontinuous or discrete • Populations can be heterogeneous, have differential learning patterns, different levels of rationality etc • Provides a natural environment for study • Spatial framework can be complex and realistic • Flexible • Can handle multiple scales, distance-related components, directional components, agent complexity etc www.spatialanalysisonline.com

  22. Agent-based modelling • Disadvantages of/issues for ABM • What is the real ‘purpose’ of model? • What is the appropriate scale for research? • How are the results to be interpreted? • How robust is the model? • Can the model be replicated? • Can the results be validated? • Are behaviours/patterns observed likely to occur in the real world? • How much is the outcome dependent on the model implementation (design, toolset, parameters etc.)? www.spatialanalysisonline.com

  23. Agent-based modelling • Choosing a simulation/modelling system • Ease of development • Size of user community • Availability of support • Availability of demonstration/template models • Availability of ‘how-to’ materials and documentation • Licensing policy (open source, shareware/freeware, proprietary) www.spatialanalysisonline.com

  24. Agent-based modelling • Choosing a simulation/modelling system • Key features • Number of agents that can be modelled • Degree of agent-agent interaction supported • Model environments (and scale) supported (network, raster, vector) • Multi-level support (agent hierarchies) • Spatial relationships support • Event scheduling/sequencing facilities www.spatialanalysisonline.com

  25. Agent-based modelling • Major simulation/modelling systems • open source: SWARM, MASON, Repast • shareware/freeware: StarLogo, NetLogo, OBEUS) • proprietary systems: AgentSheets, AnyLogic www.spatialanalysisonline.com

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