标题: A similarity law-based approach for the transferability of the urban expansion model
作者: Wang, HJ (Wang, Haijun); Liang, YT (Liang, Yaotao); Xu, S (Xu, Shan); He, SW (He, Sanwei); Zhang, B (Zhang, Bin)
来源出版物: INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE DOI: 10.1080/13658816.2025.2475329 Early Access Date: APR 2025 Published Date: 2025 APR 25
摘要: Due to spatial heterogeneity and temporal non-stationarity, urban expansion models are often only applicable locally. However, changes in urban expansion strategies will deplete the advantages of urban expansion models trained on local historical data, and there is a need to transfer reliable urban expansion models to improve their ability to simulate future scenarios. This paper proposes a similarity law-based method for transferability of urban expansion model. By leveraging geographic similarities, the proposed approach selectively transfers transition rules between cities, promoting predictive consistency across diverse local contexts. To test this approach, transition rules from Beijing were applied within the city and then transferred to other cities in the Beijing-Tianjin-Hebei region. While models with transferred transition rules showed no clear advantages in the calibration period, they performed better in the validation period compared to models using artificial neural network (ANN) or geographic weighted regression (GWR). Additional tests showed that cities benefited more from transition rules adapted from cities with comparable land use policies. These findings suggest that this approach is useful for future scenarios simulation under policy shifts and expansion strategy changes.
作者关键词: Cellular automata; geographically optimal similarity model; transferability; urban expansion strategy
KeyWords Plus: CELLULAR-AUTOMATA MODELS; LAND-USE; GROWTH; SIMULATION; TELECONNECTIONS; URBANIZATION; PREDICTION; SCENARIOS; IMPACTS; AREAS
地址: [Wang, Haijun; Liang, Yaotao] Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Peoples R China.
[Wang, Haijun] Minist Nat Resources, Key Lab Trop & Subtrop Nat Resources Monitoring So, Guangzhou, Peoples R China.
[Xu, Shan] China Agr Univ, Coll Land Sci & Technol, Beijing, Peoples R China.
[He, Sanwei] Zhongnan Univ Econ & Law, Sch Publ Adm, Wuhan, Peoples R China.
[Zhang, Bin] China Univ Geosci, Sch Publ Adm, Wuhan, Peoples R China.
通讯作者地址: Xu, S (通讯作者)，China Agr Univ, Coll Land Sci & Technol, Beijing, Peoples R China.
电子邮件地址: [email protected]
影响因子：4.3
