www.spatialanalysisonline.com chapter 8 geocomputation part a: cellular automata (ca) &...
TRANSCRIPT
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)
3rd edition www.spatialanalysisonline.com 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 www.spatialanalysisonline.com 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 www.spatialanalysisonline.com 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 www.spatialanalysisonline.com 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 www.spatialanalysisonline.com 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 www.spatialanalysisonline.com 8
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)
3rd edition www.spatialanalysisonline.com 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 www.spatialanalysisonline.com 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 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
3rd edition www.spatialanalysisonline.com 11
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)
3rd edition www.spatialanalysisonline.com 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 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’
3rd edition www.spatialanalysisonline.com 13
Cellular automata
Heatbugs (ABM framework): Click image to run model (Internet access required)
3rd edition www.spatialanalysisonline.com 14
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
3rd edition www.spatialanalysisonline.com 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 www.spatialanalysisonline.com 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 www.spatialanalysisonline.com 17
Agent-based modelling
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
3rd edition www.spatialanalysisonline.com 18
Agent-based modelling
Schelling (ABM framework): Click image to run model (Internet access required)
3rd edition www.spatialanalysisonline.com 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 www.spatialanalysisonline.com 20
Agent-based modelling
Pedestrian movement: Click image to run model (Internet access required)
3rd edition www.spatialanalysisonline.com 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 www.spatialanalysisonline.com 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 www.spatialanalysisonline.com 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 www.spatialanalysisonline.com 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 www.spatialanalysisonline.com 25
Agent-based modelling
Major simulation/modelling systems open source: SWARM, MASON, Repast shareware/freeware: StarLogo, NetLogo, OBEUS) proprietary systems: AgentSheets, AnyLogic