PROGRESS IN GEOGRAPHY ›› 2015, Vol. 34 ›› Issue (8): 937-946.doi: 10.18306/dlkxjz.2015.08.001

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Urban expansion simulation by random-forest-based cellular automata: a case study of Foshan City

CHEN Kai1,2(), LIU Kai1,2,*(), LIU Lin1,2, ZHU Yuanhui1,2   

  1. 1. Center of Integrated Geographic Information Analysis, School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
    2. Guangdong Key Laboratory for Urbanization and Geo-simulation, Guangzhou 510275, China
  • Online:2015-08-25 Published:2015-08-25
  • Contact: LIU Kai;


Cellular automata (CA) has been frequently used to investigate the logical nature of self-reproducible systems and simulate the evolution of complex geographical phenomena such as urban expansion. The core of cellular automata is to define transition rules. Traditionally, approaches for defining the transition rules of cellular automata had difficulty to balance the interpretability, accuracy, and convenience. This article presents a new cellular automata model for simulating urban expansion based on random forest algorithm. The proposed model extracts CA transition rules of urban expansion by introducing random factors in training samples and candidate spatial variables that split nodes during the multiple decision trees building process. One significant advantage of our approach is that it can be easily adopted for parallel implementation and has high prediction accuracy and tolerance to random factors in urban expansion. Another strength of the proposed approach is that it can estimate out-of-bag errors to obtain model parameters quickly and measure the importance of spatial variables and explain the contribution of each variable in urban expansion. The model was applied to simulate urban expansion in Foshan City, Guangdong Province. We used the urban land change of 1988 and 2000 as the dependent variable and the spatial variables as the independent variables to construct the CA model based on random forests, then simulate and predict urban expansion of 2000 and 2012. The results show that random forest model can improve the simulation and prediction accuracies by 1.7% and 2.6%, respectively, when compared to the logistic regression model commonly used in CA simulation. This suggests that random forest model is superior for modeling complex nonlinear urban evolution. Urban expansion of Foshan City in 2024 was also predicted according to its urban development trend. Through measuring the importance of some spatial variables that affect urban expansion, we found that distance to national roads and to the city center are the two most important spatial variables for urban expansion simulation in Foshan City.

Key words: random forest, cellular automata, urban expansion, Foshan City