The Impact of Data Time Span on Forecast Accuracy through Calibrating the SLEUTH Urban Growth Model

The Impact of Data Time Span on Forecast Accuracy through Calibrating the SLEUTH Urban Growth Model

Reihaneh Peiman (Sam Houston State University, Huntsville, Texas, USA & University of Pisa, Pisa, Italy) and Keith Clarke (University of California- Santa Barbara, Santa Barbara, California, USA)
Copyright: © 2014 |Pages: 15
DOI: 10.4018/ijagr.2014070102
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Abstract

Does the spacing of time intervals used for model input data have an impact on the model's subsequent calibration and so projections of land use change and urban growth? This study evaluated the performance of the SLEUTH urban growth and land use change model through two independent model calibrations with different temporal extents (1972 to 2006 vs. 2000 to 2006) for the historical Italian cities of Pisa Province and their surroundings. The goal in performing two calibrations was to investigate the sensitivity of SLEUTH forecasts to longer or shorter calibration timelines, that is does calibrating the model over a longer time period produce better model fits and therefore forecasts? The best fit parameters from each calibration were then used in forecasting urban growth in the area up to the year 2027. The authors findings show that the spatial growth estimated by the model was strongly influenced by the physical landscape and road networks. The forecast outputs over 100 Monte Carlo trials reflect the start of newly formed detached settlements towards and along existing roads, i.e., classic urban sprawl. The authors conclude that the short term calibration was a better model fit compared to the long term calibration. Nevertheless, the absolute preference for the short-term calibration over long-term implies that time-sensitivity in calibration remains a challenge for SLEUTH applications.
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Introduction

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).

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