The effects of factor generalization scales on the reproduction of dynamic urban growth

The production and selection of driving factors are essential to building a strong Cellular Automata (CA) model of dynamic urban growth simulation. A critical issue that should be addressed is how the spatial representation and the generalization scale of driving factors affect the CA modeling and the simulation results. It is challenging to evaluate the effectiveness of the selected driving factors because they have no true values. To explore the impacts of the generalization scales, we produced nine sets of driving factors at nine scales to calibrate the CA models based on the Particle Swarm Optimization (CAPSO) and applied them to simulate urban growth of Suzhou during 2000–2020. Our results show that the driving factors at a smaller scale have much better performance in explaining urban growth simulations as inferred by the Explained Residual Deviance (ERD) of the Generalized Additive Models (GAMs). Specifically, the ERD declined from 51.9% to 45.9% as the factor scale became larger during 2000–2020, but there was a peak value (52.2%) at Scale-2. For all simulations during 2000–2020, the CAPSO models with larger-scale factors have slightly lower overall accuracy and Figure-of-Merit (FOM), which respectively decreased by 3.1% and 4.4% as compared to the CA models with scale-free factors. We concluded that the driving factors at a smaller scale (200 ~ 400 m for point-like facilities and 7 ~ 14 m for line-like facilities) can build more accurate CA models to simulate urban growth patterns, and the optimal scale for factors can be identified using the ERD. This study contributes to the methods of evaluating the effectiveness of driving factor production and reveals the impacts of spatial representation of factors on the CA modeling and simulation considering the factor generalization scales.