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Cellular Automata Based Temporal Process
Understanding of Urban Growth
Jianquan Cheng and Ian Masser
Urban Planning and Management Division,
International Institute for Geo-Information Science and Earth Observation (ITC),
Hengelosestraat 99, P.O.Box 6, 7500 AA, Enschede,
The Netherlands
{
Jianquan, Masser
}
@itc.nl
Abstract.
Understanding of urban growth process is highly crucial in making
development plan and sustainable growth management policy. As the process
involves multi-actors, multi-behavior and various policies, it is endowed with
unpredictable spatial and temporal complexities, it requires the occurrence of
new simulation approach, which is process-oriented and has stronger capacities
of interpretation. In this paper, A cellular automata-based model is designed for
understanding the temporal process of urban growth by incorporating dynamic
weighting concept and project-based approach. We argue that this methodology
is able to interpret and visualize the dynamic process more temporally and
transparently.
1 Introduction
The city is a typical complex system, which is characterized with a self-organization
property
[1, 2]
. Understanding of urban development process is highly crucial in urban
development planning and sustainable growth management. Urban development proc-
ess involves multi-actors, multi-behaviors and various policies, which results in their
spatial and temporal complexities. Due to the hidden complexity of reality, our science
has become less orientated to prediction but more an aid to understanding, to structure
debate
[3]
. Couclelis
[4]
first put forward the idea of spatial understanding support
system (SUSS). Proper understanding of complex system is the prerequisite to its
prediction.
Cellular automata (CA), a technique developed recently, has been receiving more
and more attention in GIS modeling due to its simplicity, transparency, strong capaci-
ties for dynamic spatial simulation, and innovative bottom-up approach. Numerous
literatures can be seen even in the field of urban growth CA modeling on various
scales (regional, municipal and town) e.g.
[5-9]
.
In contrast to classic CA, more and more modifications have been made to improve
its modeling capacity such as multi-states of cell, relaxing size of neighborhood with
