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distance-decay effects, and linkage with complexity theory
[10]
. As the core of CA
model, transition rules have also been modified and expanded to include notions such
as hierarchy, self-modification, probabilistic expressions, utility maximization, acces-
sibility measures, exogenous links, inertia, and stochasticity; in fact, many-if not all-
urban CA bear little resemblance to the formal CA model
[11]
. Nevertheless, inter-
pretation of transition rules, which is highly important for urban planners, still receives
little attention in process modeling. Most studies focus on how to make complicated
models.
The previous studies of urban CA models ignore the fact that urban growth is a dy-
namic process rather than a static pattern. Similar patterns, the final outputs of CA
simulation do not indicate similar processes. Thus, the transition rules tested are not
evidential to explain the complex spatial behavior. Therefore, process rather than
pattern oriented simulation should be the major concern of urban growth CA model-
ing. This point is started to be aware in some journals
[11]
. In GIS field,
[12]
applied
fuzzy spatio-temporal interpolation to simulate changes that occurred between snap-
shots registered in a GIS database. The main advantage of the research lies in its flexi-
bility to create various temporal scenarios of urbanization processes and to choose the
desired temporal resolution. The author also declared that the approach does not ex-
plicitly provide causal factors, thus it is not an explanatory model.
In summary, we need to take spatial and temporal process into CA modelling to
achieve stronger interpretation capacities of causal factors. With this in mind, this
paper is organized into four sections. Following the introduction, the next section
discusses in detail a proposed methodology, which mainly comprises dynamic
weighting and mathematical models of local growth. One of major features in our
CA model is to utilize dynamic weighting for linking pattern and process. Sections 3
moves to the implementation of the methodology by a case study area from Wuhan
City, P.R.China. Section 4 ends with some discussion and conclusions.
2 Methodology
As a typical self-organizing social-economic system (SOS), urban system modelling
must call for an innovative bottom-up simulation approach. Complexity of urban
growth comprises the multiplicity of spatial patterns and social economic processes,
nonlinear interactions among numerous components and heterogeneity over a variety
of spatial and temporal scales. Intuitively, the complexity of urban growth process can
be transferred into spatial and temporal complexity when projected onto land system.
The understanding rather than prediction of urban growth process based on SOS
mechanisms is a feasible way. This understanding must be based on the integration of
top-down and bottom-up approach.
As an effective bottom-up simulation tool, CA firstly offers a new thinking way for
dynamic spatial modelling, and secondly provides a laboratory for testing human be-
