Modeling Urban Land Use Change: Integrating Remote Sensing with Socioeconomic Data - Computational Approaches for Urban Environments - page 285

occur (Weng 2002 ;YangandLo 2002 ). One of the greatest challenges in designing
effective urban models is that their performances are often limited by the inadequate
digital data source over time as well as the consideration of external driver such as
socioeconomic development and human disturbance (Pickett et al. 1997 ; Mcintyre
et al. 2000 ).
Remote sensing data, with the ability to provide large-scale data sources such
as historical maps or urban land use maps, has been used as an effective tool in
quantitatively measuring urban landscape and modeling urbanization at a relatively
large spatial scale (Herold et al. 2003 ;Tang 2011 ). Images from satellite sensors
provide a large amount of cost-effective multispectral and multi-temporal data to
monitor landscape changes and estimate biophysical characteristics of land surfaces
(Weng 2002 ). Many researchers have proposed the routine to combine remote
sensing with GIS in urban growth models (Tang 2011 ; Tayyebi et al. 2013 ).
Significant progress in acquiring remotely sensed data in a higher spatial resolution
and developing the spatial geographic process model has widened our research on
the process, driving forces, and impacts of the urbanization.
The cellular automata (CA) model, introduced by Tobler in 1979 , is one of the
most powerful spatial dynamics techniques used to simulate complex urban systems
(Batty and Xie 1994 ). The CA model allows researchers to view the city as a self-
organizing system in which the basic land parcels are developed into various land
use types. Cecchini and Viola ( 1990 ) applied simple decision rules in the CA model
to predict the complex, large-scale structure in the urban growth process. Wu ( 1998 )
combined the multicriteria evaluation (MCE) and GIS into the CA model to define
the transition rules in a visualized environment. Shafizadeh-Moghadam and Helbich
( 2013 ) used AHP (analytical hierarchy process) to determine the weight in a Markov
chains-cellular automata urban growth model.
The advantages of the CA model in simulating urban spatial process and
dynamics (Hillier and Hanson 1984 ; White and Engelen 1993 ) have been widely
documented because the theoretical abstraction of the CA model and the practical
constraints in the real world can be easily related (Batty and Xie 1994 ;Clarke
and Hoppen 1997 ; Wu and Martin 2002 ). The model begins from a homogeneous
cell-based grid and adjusts itself through the transition rule derived from its local
spatiotemporal neighborhood. This makes the CA model suitable to simulate com-
plex and hierarchical structures since more unknown, immeasurable spatiotemporal
variables can be incorporated and manipulated in this model. Another advantage
in CA simulation is the ability of the model to incorporate proper parameters or
weights to model the alternative socioeconomic states in the model development
(Clarke and Gaydos 1998 ;LiandYeh 2000 ). With better computer techniques, the
CA model is also able to explore more complex human behavior through defining
different transition rules (Li and Yeh 2000 ; Wu and Martin 2002 ). However, the
tension between the simple local transition rule in CA models and the complex,
unpredicted social changes in urban landscapes still remains.
In this context, this chapter attempted to develop a spatial-explicitly CA model to
simulate urban growth patterns using the classification result from Landsat images
and another one incorporated the socioeconomic data with the same classification