Complexity, Land use and Cellular Automata Modelling

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Complexity, Land use and Cellular Automata
Modelling

• Guy Engelen
• VITO Flemish Institute for Technological
  Research
• Centre for Integrated Environmental Studies
• Boeretang 200, 2400 Mol, Belgium
• guy.engelen_at_vito.be
• RIKS bv
• Abtstraat 2A, 6200 AL Maastricht, The Netherlands
• http//www.riks.nl

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Once upon a time
Dynamicspatial interaction based models
Net growth gains - losses
GIS - Geographical Information Systems
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15 years ago
Dynamicspatial interaction based models
High-resolution Spatially-dynamic
Net growth gains - losses
GIS - Geographical Information Systems
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Morphogenesis, spatial organisation and spatial
interaction (micro macro)
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Cellular Automata capture the Process behind the
morphogenesis

• Cellular Automata are explicitly dynamic,
  representing change over time
• Cellular Automata are explicitly spatial, since
  they are defined on an n-dimensional grid

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Example of a Cellular Automata Conways Life
(Gardner, 1970)
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Characteristics of CA models (1)

• Spatially-dynamic model enabling extreme spatial
  detail.
• Simple and intuitive. They start with local
  relationships and behavioural rules. Complexity
  without complication (Couclelis 1986).
• Self-organising systems with emergent properties
  locally defined rules resulting in macroscopic
  ordered structures. Massive amounts of
  individual actions result in the spatial
  structures that we know and recognise.
• Bottom-up approach to spatial modelling.
• Open. They generate a range of possible futures.
• ButThey are not normative they show what
  could happen rather than what should happen.

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Characteristics of CA models (2)

• A universal Turing machine is formally equivalent
  to a Cellular Automata CA can mimic the action
  of any physical system
• Straightforward linkage with (raster) GIS, easy
  integration of additional spatial attributes
• CA can be modelled using computers with no loss
  of precision
• CA are irreversible and irreducible . There are
  no computations or algorithms to speed up the
  cellular automata simulation. There is no way
  but simulation to predict their evolution.
• Cellular Automata models confirm that modelling
  is about exploration rather than prediction.
  Reality is simply too complex to be captured in a
  model. (sharpened intuition, informed
  speculation, and educated guess (Couclelis,
  1997)).

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Generalisation of the CA Concept

• Practical applications in the spatial sciences
  have involved the following amendments to the
  basic CA principles
• CA-space
• from infinite to finite
• from homogeneity to non-homogeneity
• regularity (cells) to irregularity (polygons).
• Neighbourhood
• from stationarity to non-stationarity
• From isotropy to non-isotropy.
• Transition function
• From universal (all cells all states) to
  non-universal
• from invariance to time variance
• from deterministic to probabilistic.
• Time steps
• from regularity to non-regularity.
• System closure
• from closed to open.

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Generalisation of the CA Concept

• Practical applications have involved the
  following amendments to the CA principles
• CA-space
• from infinite to finite
• from homogeneity to non-homogeneity
• regularity (cells) to irregularity (polygons).
• Neighbourhood
• from stationarity to non-stationarity
• from isotropy to non-isotropy.
• Transition function
• From universal (all cells all states) to
  non-universal
• from invariance to time variance
• from deterministic to probabilistic.
• Time steps
• from regularity to non-regularity.
• System closure
• from closed to open.

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CA model for Land use Change (1992-2005)

• Cellular space
• 2-D space, consisting of equally sized square
  cells (size limited by capacity of the pc).
• States are the actual land uses.3 classes
• Active states or Function states
• Passive states
• Features.
• Cells may have additional attributes
• Suitability (static / dynamic)
• Accessibility (static / dynamic)
• Zoning status (quasi static).
• Maximum 32 states
• Grid size 25 - 1000 m

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CA model for Land use Change (1992-2005)

• Circular Neighbourhood, maximum radius 8 cells.
• Circular because square neighbourhoods have
  abias towards diamond shaped spatial
  patterns(Li and Yeh, 2000, Hagen 2000)
• Larger than Moore because socio-economicinteracti
  ons take place over longer distancesthan next
  neighbours (more than diffusion alone)
• Spatial agents perceive of their immediate
  neigh-bourhood as anything between 50 and 5000
  m(real estate studies) (Urban Studies, 2001,
  Vol. 38, No. 12)
• Interactions change with distance (Geoghegan,
  2002 green areas on residential)
• Information passing in the CA. Relation between
  time and spatial expansion in the CA.
• Practical choice 8 23.
• enables studying the importance of resolution
  (Grass, 1996)
• multi-raster applications and linkages with other
  modelling approaches (Lorek, Sonnenschein)
• provides better solutions for Patch problems.

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CA model for Land use Change (1992-2006)

• Transition rules representing
• Locational preferences of spatial agents in
  competition for space
• Appreciation of the proximity of other competing
  or befriended activities and static elements in
  the immediate neighbourhood
• Willingness to develop or give-up activity in a
  particular location.

Commerce
Water
Housing
Forest
Industry

• Push and pull forces,agglomeration benefits,
  inertia, etc
• The rule set consists of all the significant
  rules(2-3/function suffices)
• Interaction weights have relative value
  withinthe model only.

Commerce
Industry
Housing
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Transition rules

• Rules express the locational preferences and
  locational behaviour of spatial agents in a
  survival of the fittest situation
• Rules express their willingness to produce or
  give up floor space (see also Webster and Wu,
  2001)
• Rules express (on a relative scale) how they
  value the presence of other functions and land
  uses / land covers in their neighbourhood.
• Effect at distance 0 of the function on itself
• Inertia expressing the strength with which the
  existing land use will stick to its present
  location.
• Effect at distance 0 of any other function on
  the function
• Ease of re-conversion the ease with which a new
  land use will take over from the existing land
  use.

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Transition rules
Effects at distance gt 0 No interaction Attracti
on positive agglomeration benefits diminishing
with distance. Repulsion negative agglomeration
benefits diminishing with distance Change in
type of interaction from attraction to repulsion
or/ and vice versa. Strong interaction with far
neighbours, abruptly falling Gradual
decay, Strong interaction with immediate
neighbours, gradually falling Sphere of
influence short tail the interaction is limited
to short distances long tail the interaction
effect works over longer distances.
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Transition Rule Change cells to land-use for
which they have the highest transition potential
untill the demands are met.
Time Loop
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Bottom-up Agent based approach to morphogenesis
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Simulating the growth of Cincinnati from 1840
till 1960
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Simulation (left) vs. Reality (right)
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Transition Rule Change cells to land-use for
which they have the highest transition potential
untill the demands are met.
Time Loop
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ConstrainedCellular Automata

• The Cellular Automata dynamics evolve in a
  non-homogeneous geographical space defined by GIS
  attributes and layers (see also most of the
  others)
• Their overall dynamics are not determined by the
  micro Cellular Automata transition rules, but
  by processes at a larger macro scale (see also
  most of the others)
• Cellular Automata models have been integrated
  with more traditional dynamic models, which in
  the most general case are regionalised (spatial
  interaction based) (Engelen et al., 1993).

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EU-JRC MOLAND Modelling Framework(Example
Greater Dublin)

• Scenarios on Demographic growth and on jobgrowth
  in 3 economic sectors (Industry, Commerce and
  Services) for the whole area.

Global Greater Dublin
Macro

• Dynamic Spatial interaction-based model.
  Allocates and re-allocates the population and the
  economic activities among the 9 counties.
• Takes regional information on population and jobs
  as well as local information on the quantity and
  quality of space as an input.

Regional 9 Counties

• Cellular Automata based model. Takes the regional
  figures as an input and allocates the population
  and jobs to cellular units.
• Returns aggregate information to the regional
  model relative to the quantity and quality of the
  space available in the counties.

Local 630,000 cells of 4ha each
Micro
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Transition Rule Change cells to land-use for
which they have the highest transition potential
until Regional demands are met.
Time Loop
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Macro-level - RegionalDynamic spatial
interaction based
f ( )
All economic activities, jobs, population, zoning
, suitability, accessibility, in zone and at a
distance
For each of the 9 counties, this model calculates
on a yearly basis the changing number of
inhabitants and jobs in the selected economic
sectors.
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Macro-level - GlobalGrowth scenarios
Scenarios on demographic and job growth in the
selected economic sectors (Industry, Commerce and
Services) for the whole modelled area
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Building a new application
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Assessing planning alternativesDynamic
indicators at several geographical levels
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MOLAND Flooding risks in Pordenone, Italy (EU-JRC)
Pordenone 2002 floods
(source Brezger, 2004)
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MOLAND Flooding risks in Pordenone, Italy (EU-JRC)
Floodable areas in 2000
Urbanised in 2000
F River bed. Frequently flooded P3 Very high
risk. Areas close to dikes P2 High risk.
Critical area with 100-year event discharges P1
Moderate risk. Areas flooded in 2002 and 1996.
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MOLAND Flooding risks in Pordenone, Italy (EU-JRC)
Population density (inh/ha) in 2000
Land use in 2000
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MOLAND Flooding risks in Pordenone, Italy (EU-JRC)
Urbanised in 2000
Urbanised in 2020
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MOLAND Flooding risks in Pordenone, Italy (EU-JRC)
Floodable areas

Land use in the floodable areas
jaar
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MOLAND Flooding risks in Pordenone, Italy (EU-JRC)
Flooding risk

• Dangerous cocktail
• Urbanisation in the floodable area
• Increased frequency and importance of extreme
  weather events caused by climate change.

Urbanised area per risk category
jaar
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What else other CA-work is there at RIKS?

• Calibrations, validations of various applications
  have shown that the Constrained Cellular Automata
  modelling methodology and type of models works
  fine in general
• That these models can be readily and usefully
  applied for practical planning purposes
• Models have been developed that integrate
  physical processes, represented at the cellular
  level, enabling to work with a dynamic
  suitability
• Models have been developed that have a so-called
  two-step land use allocation
• Models have been developed that combine
  Individual Based Models (IBM) with Cellular
  Automata (Beach Plover Model and Lobster model)

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Variable grid Cellular Automata the way ahead?

• Applications coupling CA with spatial interaction
  based models work fine for areas consisting of
  reasonably uniform, equally sized zones (ex. The
  Netherlands)
• They work less well for areas consisting of
  widely different zones (ex. Larger Dublin
  Metropolitan Area)

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The solutionEliminate the regions?

• Each cell is allocated a certain amount of the
  activity corresponding to its land use. Real
  cell densities can be worked with, rather than
  regional densities
• The cell neighbourhood is expanded to include the
  entire area modelled.

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The solutionEliminate the regions?

• Each cell is allocated a certain amount of the
  activity corresponding to its land use. Real
  cell densities can be worked with, rather than
  regional densities
• The cell neighbourhood is expanded to include the
  entire area modelled.
• The neighbourhood weighting functions now capture
  the distance decay effects previously represented
  in the macro model.
• Each cell is now effectively its own region,
  competing with all others for activity.
• Cell figures are aggregated per administrative
  entities to enable usage at the macro-level, but
  also calibration and validation.
• First results seem promising, but more research
  work is required, starting next Monday -)

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The END