Simulating urban growth processes by integrating cellular automata model and artificial optimization in Binhai New Area of Tianjin, China

Abstract

This study presents an optimized algorithm into the cellular automata (CA) models for urban growth simulation in Binhai New Area of Tianjin, China. The optimized CA model by particle swarm optimization (PSO) was compared with the logistic-based cellular automata (LOGIT-CA) model to see the effects of the simulation. The study evaluated the stochastic disturbance in the development of urban growth using the Monte Carlo method; the coefficient d determined the state of urban growth. The validation was conducted by both cross-tabulation test and structural measurements. The results showed that the simulations of PSO-CA were better than LOGIT-CA model, indicating an improvement in the spatio-temporal simulation of urban growth and land use changes in study area. Since the simulations reached their best values when the coefficient was between 1 and 2, the urban growth in the study area was in the period of conversion from spontaneous growth to edge-expansion and infilling growth.

Keywords:

• urban growth
• cellular automata
• particle swarm optimization (PSO)
• simulation

Acknowledgements

The authors would like to thank the anonymous reviewers and the editor for their valuable comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the CAS-TWAS Project for Drought Monitoring and Assessment [grant number Y3YI2701KB]; the 1-3-5 Innovation Project of RADI_CAS [grant number Y3ZZ15101A]; the Innovation Fund of CEODE Director [grant number Y2ZZ26101B]; the 100 Talent Program of Chinese Academy of Sciences [grant number No.Y24002101A]; the CAS Xinjiang Location Cooperation Project [grant number Y423011010A].