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## Chapter 8

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### Chapter 8

Geocomputation Part A:

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

www.spatialanalysisonline.com

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

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

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

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

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

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

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

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

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 tntn+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

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

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 tntn+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

Cellular automata

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

www.spatialanalysisonline.com

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

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

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

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

Agent-based modelling

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

www.spatialanalysisonline.com

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

Agent-based modelling

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

www.spatialanalysisonline.com

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

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

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

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

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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