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

2. Geocomputation
“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)
3rd edition

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!
3rd edition

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
3rd edition

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
3rd edition

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
3rd edition

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)
3rd edition

8. Cellular automata State variables Spatial framework
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)
3rd edition

9. Cellular automata Neighbourhood structure
Typically Moore (8-way) or von Neumann (4-way)
Typically lag=1 but lag=2 .. and alternatives are possible
3rd edition

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
3rd edition

11. (still evolving – to density 4%)
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%)
3rd edition

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’
3rd edition

13. Cellular automata
Heatbugs (ABM framework): Click image to run model (Internet access required)
3rd edition

14. Cellular automata Example geospatial modelling applications: But..
Bushfires
Deforestation
Earthquakes
Rainforest dynamics
Urban systems
But..
Not very flexible
Difficult to adequately model mobile entities (e.g. pedestrians, vehicles)… interest in ABM
3rd edition

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
3rd edition

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.) …
3rd edition

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
3rd edition

18. Agent-based modelling
Schelling (ABM framework): Click image to run model (Internet access required)
3rd edition

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
3rd edition

20. Agent-based modelling
Pedestrian movement: Click image to run model (Internet access required)
3rd edition

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
3rd edition

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.)?
3rd edition

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)
3rd edition

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
3rd edition

25. Agent-based modelling
Major simulation/modelling systems
open source: SWARM, MASON, Repast
shareware/freeware: StarLogo, NetLogo, OBEUS)
proprietary systems: AgentSheets, AnyLogic
3rd edition