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Models form a bridge between description and analysis on the one hand, and explanation and prediction on the other (Clarke et al., 2007). Models are essential components to understand the dynamics of urban growth, and one way to conduct experiments. The processes underlying land use change are able to generate aggregate structures and future patterns that are not reflected at the level of the individual system constituents, exhibiting the property of emergence (Holland, 1998). Using a complex systems approach (Silva, 2010), a variety of spatial scales, including global and superregional urban networks, metropolitan agglomerations, as well as urban growth and land use change within individual cities at the local level can be addressed by urban modeling and through theories of urban dynamics (Alberti, 1999; Alberti & Waddell, 2000). The simplest models to exhibit these emergent and scaling properties are cellular automata (CA).
Couclelis (1985; 1997) has strongly advocated the use of CA as potentially powerful contributors to urban systems modeling. CA operation is based on an array of identically programmed automata or cells that exist in one of a finite number of states (Park & Wagner, 1997; Clarke & Gaydos, 1998). During recent years, the CA method-in combination with the use of geographic information systems (GIS) as tools of data assimilation-has been widely applied to analyze and simulate urban growth. Despite criticism of the first generation of urban computer models (Lee, 1973; Lee, 1994), computational CA models have proven useful in urban planning. In reality, the complexity of the process underlying urban growth results in significantly different characteristics from city to city, so that modeling results derived from heterogeneous applications are generally incomparable (Benenson & Torrens, 2004). Thus good CA models are those that can be made to adapt common behaviors to the spatial heterogeneity of their application environments.
The SLEUTH model employs a modified CA to simulate the spread of urbanization across a landscape (Clarke et al., 1997; Clarke & Gaydos, 1998). The model was initially applied to North American cities such as San Francisco (Clarke et al., 1997), Chicago, the Washington-Baltimore area (Clarke & Gaydos, 1998), Sioux Falls, central California (Tietz et al., 2005), and Philadelphia (Varanka, 2001). Subsequently, other examples of the application of the SLEUTH model to various worldwide case studies include: Lisbon and Porto (Silva & Clarke, 2002) in Portugal (Europe), Porto Alegre (Leao, 2002) in Brazil (South America), Cape Town in South Africa (Watkiss, 2008), Chang Mai (Sangawongse, 2006), Thailand (Asia), and Sydney (Liu and Phinn, 2004), Australia.
Over a decade of cellular modeling with SLEUTH, the model has found many improvements in its own performance to the extent that its limitations have been greatly reduced in comparison to the first applications of the model (Clarke 2008a, b). Today SLEUTH is used not only for local applications, but also for forecasting urban development at regional and continental scales, and for informal settlements (Sietchiping, 2004).