![]() Yeh and Li proposed a PCA-CA (principal component analysis) model and simulated the land use prospects of Dongguan City guided by five different planning goals. Clarke and Gaydos added an “exclusive layer” to their SLEUTH model to simulate growth control in planning and applied the model to Washington–Baltimore. Wu developed an MCE-CA (multi-criteria evaluation) model that is capable of simulating various development scenarios by adjusting multi-dimensional and multi-level influencing factors, and applied it to Guangzhou City. ![]() White and Engelen developed a planning support system by integrating constrained CA (socio-economic constraints) and Geographic Information System. Our experiment suggests that, at least in some cases, urban growth modeling at a larger spatial extent can yield better results than merely modeling the area of interest, and the impacts of the spatial extent of simulation should be considered by modelers.Īttempts have been made in the application of CA models to provide support for urban planning decision making. Comparisons between the simulation results and the actual urban growth in the study area from 2005 to 2015 show that the accuracy of the city model was 7.33% higher than the village model and the latter had more errors in simulating the growth of small clusters. Urban growth CA models are built and trained at the spatial extent of the village and the whole city. ![]() We used five villages at the rural–urban fringe in Chengdu, China as the case study. To tackle this gap, in this paper, the impact of the simulation of spatial extent on simulation performance is tested and discussed. However, it has been a common practice that the simulation is conducted at and only at the spatial extent where the results are needed, while as we know, urban development in one place can also be influenced by the situations in the broader contexts. Previous studies have extensively discussed various model building and calibration techniques to improve simulation performance. For this purpose, cellular automata-based (CA) simulation tools have been widely developed and applied. The accurate prediction of urban growth is pivotal for managing urbanization, especially in fast-urbanizing countries. ![]()
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