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

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chapter 8

Chapter 8

Geocomputation Part A:

Cellular Automata (CA) & Agent-based modelling (ABM)


“the art and science of solving complex spatial problems with computers”

Key new areas of geocomputation:

Presentation 8A: Geosimulation (CA and ABM)

Presentation 8B: Artificial Neural Networks (ANNs); & Evolutionary computing (EC)


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!

geocomputation geosimulation
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

geocomputation anns
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

geocomputation evolutionary computing
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

cellular automata ca
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)

cellular automata
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)

cellular automata1
Cellular automata

Neighbourhood structure

  • Typically Moore (8-way) or von Neumann (4-way)
  • Typically lag=1 but lag=2 .. and alternatives are possible

cellular automata2
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

cellular automata3
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%)

cellular automata4
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’

cellular automata5
Cellular automata

Heatbugs (ABM framework): Click image to run model (Internet access required)

cellular automata6
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

agent based modelling
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

agent based modelling1
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.) …

agent based modelling2
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

agent based modelling3
Agent-based modelling

Schelling (ABM framework): Click image to run model (Internet access required)

agent based modelling4
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

agent based modelling5
Agent-based modelling

Pedestrian movement: Click image to run model (Internet access required)

agent based modelling6
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

agent based modelling7
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.)?

agent based modelling8
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)

agent based modelling9
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

agent based modelling10
Agent-based modelling
  • Major simulation/modelling systems
    • open source: SWARM, MASON, Repast
    • shareware/freeware: StarLogo, NetLogo, OBEUS)
    • proprietary systems: AgentSheets, AnyLogic