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

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www.spatialanalysisonline.com Chapter 8 Geocomputation Part A: Cellular Automata (CA) & Agent-based modelling (ABM) Slide 2 3 rd editionwww.spatialanalysisonline.com2 Geocomputation the art and science of solving complex spatial problems with computers www.geocomputation.org www.geocomputation.org Key new areas of geocomputation: Presentation 8A: Geosimulation (CA and ABM) Presentation 8B: Artificial Neural Networks (ANNs); & Evolutionary computing (EC) Slide 3 3 rd editionwww.spatialanalysisonline.com3 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! Slide 4 3 rd editionwww.spatialanalysisonline.com4 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 Slide 5 3 rd editionwww.spatialanalysisonline.com5 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 Slide 6 3 rd editionwww.spatialanalysisonline.com6 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 Slide 7 3 rd editionwww.spatialanalysisonline.com7 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) Slide 8 3 rd editionwww.spatialanalysisonline.com8 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) Slide 9 3 rd editionwww.spatialanalysisonline.com9 Cellular automata Neighbourhood structure Typically Moore (8-way) or von Neumann (4-way) Typically lag=1 but lag=2.. and alternatives are possible Slide 10 3 rd editionwww.spatialanalysisonline.com10 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 t n t n+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 state2 or 3 in neighbourhood then state 0 Slide 11 3 rd editionwww.spatialanalysisonline.com11 Cellular automata t 0 35% cell occupancy Randomly assigned t n evolved pattern (still evolving to density 4%) Life (ABM framework): Click image to run model (Internet access required) Slide 12 3 rd editionwww.spatialanalysisonline.com12 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 t n t n+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 Slide 13 3 rd editionwww.spatialanalysisonline.com13 Cellular automata Heatbugs (ABM framework): Click image to run model (Internet access required) Slide 14 3 rd editionwww.spatialanalysisonline.com14 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 Slide 15 3 rd editionwww.spatialanalysisonline.com15 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 Slide 16 3 rd editionwww.spatialanalysisonline.com16 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.) Slide 17 3 rd editionwww.spatialanalysisonline.com17 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