Modeling Urban Land Use Change: Integrating Remote Sensing with Socioeconomic Data - Computational Approaches for Urban Environments - page 289

social variables in predicting land use/cover change in the urban fringe of Morelia
city, Mexico. Weng ( 2002 ) demonstrated that the integration of satellite remote
sensing and GIS techniques into the stochastic urban modeling was an effective
approach for analyzing the direction, rate, and spatial pattern of landscape change in
Zhujiang Delta of China. Tang et al. ( 2007 ) improved the Markov chain model by
incorporating a modified genetic algorithm in the urban boundary expansion for
urban simulation. Mathematically, most vector-based models rely on some static
equations, and this characteristic provides the potential in integrating the statistical
information into the model entities. The major drawbacks of such models are the
poor handling in dynamic entities and poor representation of external variables, e.g.,
the spatial information and socioeconomic factors.
The models developed on grid have more advantage in solving these problems
than the vector ones. Land-Use Change Analysis System ( LUCAS ) is a grid-based
model which integrates socioeconomic and ecological variables in the multilayered,
gridded maps (Berry et al. 1996 ). This model consists of three subject modules:
socioeconomics, which derives the transition probability from the function of
socioeconomic driving variables; landscape change, which predicts the landscape
maps from the socioeconomic module; and environmental impacts, which estimates
the impacts of selected environmental variables from the landscape maps from
second modules. Land Transformation Model ( LTM ) (Pijanowski et al. 1997 , 2014 )
applied the spatial rules to land use transitions for each location in the processed
spatial layer or grid. It is easy to quantify the contribution of different spatial
variables because of its grid format. In order to aggregate the land use change and
change drivers, this model adopted the similar method with the Conversion of Land
Use and its Effects ( CLUE ) model (De Kong et al. 1999 ). Both of them apply the
variable values in grid format to create a series of future land use patterns over
the time. Cellular automata model has been proposed and developed to simulate
the urban land use model by incorporating various socioeconomic variables, such as
dynamic transportation model (Aljoufie et al. 2013 ) and dynamic population density
(Van Vliet et al. 2012 ).
Agent-based model (Liebrand et al. 1998 ) is a complex behavior model which
used both vector data and raster data. Usually, the raster data is the agents'
environment, and the agents, in turn, act on the simulated environment. This model
can be applied to a wide variety of simulations, including moving cars, animals,
people, or even organizations. The socioeconomic variable, as both agents' status
and driving forces, was incorporated into the model to simulate individual activities
(An et al. 2005 ). This model is difficult to develop and control since we need to
incorporate the “individual agent” information and predict its potential behaviors.
Generally, a reliable urban growth model should have the following capabilities:
(1) providing an appropriate theoretical and technical framework for urban growth;
(2) understanding and describing the historical dynamics of urban structures; and (3)
exploring and incorporating different economic and social parameters to monitor the
urban growth.