Geocomputation Part A:

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Chapter 8

• Geocomputation Part A
• Cellular Automata (CA) Agent-based modelling
  (ABM)

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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)

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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!

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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

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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

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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

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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)

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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 lag1 but lag2 .. 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)

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Cellular automata

• Neighbourhood structure
• Typically Moore (8-way) or von Neumann (4-way)
• Typically lag1 but lag2 .. and alternatives are
  possible

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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 (rooks move)
  cells
• State transition rules time tn?tn1
• Survival if state1 and in neighbourhood 2 or 3
  cells have state1 then state ? 1 else state ? 0
• Reproduction if state0 but state3 or 4 in
  neighbouring cells then state ? 1
• Death (loneliness or overcrowding) if state1
  but stateltgt2 or 3 in neighbourhood then state ? 0

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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)
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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?tn1
• 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

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

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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

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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.)

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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
  ltx then move to an unoccupied cell or leave
  entirely

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Agent-based modelling
Schelling (ABM framework) Click image to run
model (Internet access required)
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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

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Agent-based modelling
Pedestrian movement Click image to run model
(Internet access required)
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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

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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.)?

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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)

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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

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Agent-based modelling

• Major simulation/modelling systems
• open source SWARM, MASON, Repast
• shareware/freeware StarLogo, NetLogo, OBEUS)
• proprietary systems AgentSheets, AnyLogic