Abstract
Cellular automata (CA) is a spatially explicit modeling tool that has been shown to be effective in simulating urban growth dynamics and in projecting future scenarios across scales. At the core of urban CA models are transition rules that define land transformation from non-urban to urban. Our objective is to compare the urban growth simulation and prediction abilities of different metaheuristics included in the R package optimx. We applied five metaheuristics in optimx to near-optimally parameterize CA transition rules and construct CA models for urban simulation. One advantage of metaheuristics is their ability to optimize complexly constrained computational problems, yielding objective parameterization with strong predictive power. From these five models, we selected conjugate gradient-based CA (CG-CA) and spectral projected gradient-based CA (SPG-CA) to simulate the 2005–2015 urban growth and to project future scenarios to 2035 with four strategies for Su-Xi-Chang Agglomeration in China. The two CA models produced about 86% overall accuracy with standard Kappa coefficient above 69%, indicating their good ability to capture urban growth dynamics. Four alternative scenarios out to the year 2035 were constructed considering the overall effect of all candidate influencing factors and the enhanced effects of county centers, road networks and population density. These scenarios can provide insight into future urban patterns resulting from today’s urban planning and infrastructure, and can inform future development strategies for sustainable cities. Our proposed metaheuristic CA models are also applicable in modeling land-use and urban growth in other rapidly developing areas.
Highlights
Spatial explicit modeling (SEM) has been effective and increasingly applied in studies of land-use change [1,2], in rapidly urbanizing areas like China’s coastal zones
The R package optimx provides a unified framework that is suitable for solving optimization problems
We developed five cellular automata (CA) models for simulating urban growth using the metaheuristics included in optimx, which automatically search for the near-optimal parameters in defining land transition rules
Summary
Spatial explicit modeling (SEM) has been effective and increasingly applied in studies of land-use change [1,2], in rapidly urbanizing areas like China’s coastal zones. Among the SEM methods, cellular automata (CA) has become the most widely used approach to first reproduce past landscape and urban patterns and predict future scenarios under specific development strategies [3,4,5,6]. CA models have provided a basis for rational urban spatial expansion that could reduce conflicts between land exploitation and conservation [7]. The transition rules can be parameterized using approaches that range from conventional statistical techniques to state-of-the-art artificial intelligence algorithms [12]. While many specific methods have been integrated with CA modeling, it is necessary to compare models with a unified framework
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