chapter8A - Geospatial Analysis

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Chapter 8

Geocomputation Part A:

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

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rd

edition

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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)

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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!

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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

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rd

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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

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rd

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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

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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)


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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)

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Cellular automata

Neighbourhood structure


Typically Moore (8
-
way) or von Neumann (4
-
way)


Typically lag=1 but lag=2 .. and alternatives are possible

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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 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
state<>2 or 3 in neighbourhood then state


0

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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)

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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’

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Cellular automata

Heatbugs

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

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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


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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


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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.) …

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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





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Agent
-
based modelling

Schelling

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

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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

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Agent
-
based modelling

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

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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


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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.)?


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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)

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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

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Agent
-
based modelling



Major simulation/modelling systems


open source:
SWARM
,
MASON
,
Repast


shareware/freeware:
StarLogo
,
NetLogo
,
OBEUS
)


proprietary systems:
AgentSheets
,
AnyLogic