Validating Spatial Patterns of Urban Growth from a Cellular Automata Model

The dynamics of urban growth are the direct consequence of the actions of individuals, and public and private organisations, which act to change the urban landscape simultaneously over space and time. Since previous urban form has a strong influence on the present, a prime concern of urban planners, spatial scientists and government authorities is to under‐ stand how a city has grown in the past in order to predict the growth of the city in the fu‐ ture. This requires flexible tools that allow planners to examine the impacts and potential consequences of applying different development policies, strategies and future plans [1]. However, traditional linear, static and top-down models are unable to adequately capture the processes underlying urban change. The non-linearity of spatial and temporal relation‐ ships and irregular, uncoordinated and uncontrolled local decision-making gives rise to seemingly coordinated global patterns that define the size and shape of cities in familiar ways [2-7]. Cities are now increasingly recognized as complex systems and display many of the characteristic traits of complexity, i.e. non-linearity, self-organization and emergence. Cellular Automata (CA) offer a modeling framework and a set of techniques for modelling the dynamic processes and outcomes of self-organizing systems [8-13]. Since the late 1980s they have demonstrated significant potential benefits for urban modelling through their simplicity, flexibility and transparency [8, 14-17]. CA are capable of generating complex pat‐ terns in aggregate form by using relatively simple local transition rules, i.e. by recursive de‐ velopment decisions being made at individual cells or sites [2, 15, 18]. However, cities are also influenced by global factors representing government polices (such as broader social, economic and technological factors). This has led to a number of hybrid-type urban growth models, which take into consideration local, regional and global factors [19-22]. When inte‐ grated with other technologies such as GIS and remote sensing, the potential of CA for geo‐