区域尺度城市增长时空动态模型及其在京津唐都市圈应用

Dynamic urban expansion simulation at regional scale is one of the important re-search methodologies in Land Use/Cover Change (LUCC) and global environmental change influenced by urbanization.However,previous studies indicate that the single urban expan-sion simulation for future scenarios at local scale cannot meet the requirements for charac-terizing and interpreting the interactive mechanisms of regional urbanization and global en-vironmental change.This study constructed a regional Dynamic Urban Expansion Model (Reg-DUEM) suitable for different scenarios by integrating the Artificial Neural Network (ANN) and Cellular Automaton (CA) model.Firstly we analyzed the temporal and spatial character-istics of urban expansion and acquired a prior knowledge rules using land use/cover change datasets of Beijing-Tianjin-Tangshan metropolitan area.The future urban expansion under different scenarios is then simulated based on a baseline model,economic models,policy models and the structural adjustment model.The results indicate that Reg-DUEM has good reliability for a non-linear expansion simulation at regional scale influenced by macro-policies.The simulating results show that future urban expansion patterns from different scenarios of the metropolitan area have the tremendous spatio-temporal differences.Future urban ex-pansion will shift quickly from Beijing metropolis to the periphery of Tianjin and Tangshan city along coastal belt.

Simulating dynamic urban expansion at regional scale in Beijing-Tianjin-Tangshan Metropolitan Area

Dynamic urban expansion simulation at regional scale is one of the important research methodologies in Land Use/Cover Change (LUCC) and global environmental change influenced by urbanization. However, previous studies indicate that the single urban expansion simulation for future scenarios at local scale cannot meet the requirements for characterizing and interpreting the interactive mechanisms of regional urbanization and global environmental change. This study constructed a regional Dynamic Urban Expansion Model (Reg-DUEM) suitable for different scenarios by integrating the Artificial Neural Network (ANN) and Cellular Automaton (CA) model. Firstly we analyzed the temporal and spatial characteristics of urban expansion and acquired a prior knowledge rules using land use/cover change datasets of Beijing-Tianjin-Tangshan metropolitan area. The future urban expansion under different scenarios is then simulated based on a baseline model, economic models, policy models and the structural adjustment model. The results indicate that Reg-DUEM has good reliability for a non-linear expansion simulation at regional scale influenced by macro-policies. The simulating results show that future urban expansion patterns from different scenarios of the metropolitan area have the tremendous spatio-temporal differences. Future urban expansion will shift quickly from Beijing metropolis to the periphery of Tianjin and Tangshan city along coastal belt.