www.spatialanalysisonline.com chapter 8 geocomputation part a: cellular automata (ca) &...

www.spatialanalysisonline.com

Chapter 8

Geocomputation Part A:

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

3rd edition 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)


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!


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

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

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)

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 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 automata

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


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

1. Survival: if state=1 and in neighbourhood 2 or 3 cells have state=1 then state 1 else state 0

2. Reproduction: if state=0 but state=3 or 4 in neighbouring cells then state 1

3. Death (loneliness or overcrowding): if state=1 but state<>2 or 3 in neighbourhood then state 0


Cellular automata

t0 35% cell occupancy

Randomly assigned

tn – evolved pattern

(still evolving – to density 4%)

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


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

1. Bugs can move, but only to an adjacent cell that does not have a bug on it

2. Bugs move if they are ‘unhappy’ – too hot or too cold (if they can move to a better adjacent cell)

3. Bugs emit heat (min and max rates settable)4. Heat diffuses slowly through the grid and some is lost to

‘evaporation’


Cellular automata

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


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

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



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.) …



Example 1: Schelling segregation modelActually 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



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



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



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



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



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.)?



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)



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



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

www.spatialanalysisonline.com chapter 8 geocomputation part a: cellular automata (ca) &...

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cells state transition

welldefined state

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heatbugs state variables

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