Cellular Automata and Urban LUCC Simulation

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Cellular Automata and Urban LUCC Simulation
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1. Land Use and Land Cover Change (LUCC)
2. Urban Models
3. Cellular Automata Theory, Components,
and Mechanics
4. Cellular Automata for Urban LUCC Simulation
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1. Land Use and Land Cover Change (LUCC)
Land use and land cover change, is one of the
main driving forces of global environmental
change. It has impacts on a wide range of
environmental and landscape attributes including
the quality of water, land and air resources,
ecosystem processes and function, and the climate
system itself through greenhouse gas fluxes and
surface albedo effects.
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1. Land Use and Land Cover Change (LUCC)
In the analysis of land use and land cover
change, it is first necessary to conceptualize
the meaning of change to detect it in real world
situations. At a very elementary level, land use
and land cover change means (quantitative)
changes in the areal extent (increases or
decreases) of a given type of land use or land
cover, respectively. It is important to note
that, even at this level, the detection and
measurement of change depends on the spatial
scale the higher the spatial level of detail,
the larger the changes in the areal extent of
land use and land cover which can be detected and
recorded.
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2. Urban Models
The field of urban modeling has a relatively
short history compared to other sciences like
biology, chemistry, and physics. Many early
attempts at modeling (and some current ones) were
extremely complex mathematical models, with a
number of equations linked together to try to
explain how a city functioned. They required a
large amount of data and generally the use of
computer power that was extreme for the time.
These problems led to a decline in the modeling
of urban systems.
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2. Urban Models
Recent advances in simulation techniques
including cellular automata have revived the
field and let to a wider use within the academic
community. While the integration of models in the
planning process has not yet materialized, the
rise of Planning Support Systems (PSS), with
urban models integrated into them, has helped to
bring some of these models to the public. Much of
the theory in urban models has been built on the
foundation of some of the very first models they
provide a general basis from which contemporary
applied urban modeling has come from.
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2. Urban Models
More recently geographers have moved away from
the early theoretical models, to more applied
urban growth models. MEPLAN TRANUS UrbanSim These
models are just a few of a wide array available
for modeling urban systems.
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2. Urban Models
While the models mentioned in this section are
some of the most prominent in urban and regional
modeling, and attempt to model the same systems
as cellular automata, they are not cellular
automata models, and there are several
distinctions between the two groups.
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3. Cellular Automata Theory, Components, and
Mechanics
Cellular automata (CA) are mathematical models
for complex natural systems containing large
numbers of simple components with local
interactions. These models enable the examination
of systems whose behavior at the global scale can
be classified as complex, while at the local or
individual level they are comprised of
fundamentally simple components.
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3. Cellular Automata Theory, Components, and
Mechanics

• CA models are comprised of four individual
  components, all of which are required to be
  classified as a cellular automata.
• 1. Lattice or cell space
• 2. Local states
• 3. Transition rules
• 4. Neighborhood
• In recent years other researchers have added an
  additional fifth component to CA, although it is
  naturally adherent to their nature.
• 5. Temporal space (time)

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3. Cellular Automata Theory, Components, and
Mechanics

• Advantages of Cellular Automata for Spatial Models

1. The most obvious is that CA models are spatial
in the same manner that an urban or any other
geographic system is. 2. The spatial aspect of
CA is a natural link to geographic and remotely
sensed data, much of which is used as input for
these models. 3. The process of simultaneous
computation in CA allows modelers to view urban
systems growing over time in increments instead
of just the beginning and end points. 4. CA act
within a localized neighborhood, creating
micro-scale dynamics but when the overall
micro-scale behavior of the system is taken
collectively, macro-scale patterns emerge. 5.
The lattice structure and link to geographic and
remotely sensed data makes CA models highly
visual giving modelers and model users the
ability to visualize the results of model
forecasts.
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Game of Life
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4. Cellular Automata for Urban LUCC Simulation
 The logic and mechanism of CA allows linking the
local to the global, not just through simulation
and model forecasting, but in the sense that
global patterns and forms can be generated
through local processes. The idea of cellular
automata-like models, simulating change at the
local scale, can be traced to Tobler (1979), but
a more formal outline of CA models and their
possible use in simulating urban systems was made
by Couclelis (1985). While the use of CA for
urban systems were slow to catch on, taking
nearly a decade before there was a broad body of
literature, adaptation, experimentation, and
application of these models to urban systems has
been quite common in recent years.
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4. Cellular Automata for Urban LUCC Simulation
  The Dynamic Urban Evolutionary Model (DUEM) was
initially developed as part of Xies (1994)
dissertation work at SUNY Buffalo, and further
expanded in recent years (Xie 1996 Xie and Batty
1997 Batty et al. 1999). DUEM differs from
standard urban CA models because it deals with a
comprehensive series of land uses, one of which
is transportation infrastructure (Xie and Batty
2003). Land uses have three separate phases
within the model, where land use change is
initiated, matures, and then declines where it
can be changed to some other land use class.
Change between land use classes is driven by land
supply, and generated by two process one land
use initiates other land uses around it, or one
land use transforms itself into another in the
same location.
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4. Cellular Automata for Urban LUCC Simulation
The idea of linking a CA model with other urban
and regional models that are founded in stronger
socio-economic theory is a major contribution of
this work. The four models provide something
unique to urban CA modeling, and lay the
foundation for future model development and
application.
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Zhangjiagang City(2006)
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Zhangjiagang City(2016)
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