Development Process, Quantitative Models, and Future Directions in Driving Analysis of Urban Expansion.
- Resource Type
- Article
- Authors
- Guan, Xuefeng; Li, Jingbo; Yang, Changlan; Xing, Weiran
- Source
- ISPRS International Journal of Geo-Information. Apr2023, Vol. 12 Issue 4, p174. 25p.
- Subject
- *URBAN growth
*SOCIAL dynamics
*URBAN planning
*BIG data
*URBANIZATION
*REMOTE sensing
*SOCIOECONOMIC factors
*MACHINE learning
- Language
- ISSN
- 2220-9964
Driving analysis of urban expansion (DAUE) is usually implemented to identify the driving factors and their corresponding driving effects/mechanisms for the expansion processes of urban land, aiming to provide scientific guidance for urban planning and management. Based on a thorough analysis and summarization of the development process and quantitative models, four major limitations in existing DAUE studies have been uncovered: (1) the interactions in hierarchical urban systems have not been fully explored; (2) the employed data cannot fully depict urban dynamic through finer social perspectives; (3) the employed models cannot deal with high-level feature correlations; and (4) the simulation and analysis models are still not intrinsically integrated. Four future directions are thus proposed: (1) to pay attention to the hierarchical characteristics of urban systems and conduct multi-scale research on the complex interactions within them to capture dynamic features; (2) to leverage remote sensing data so as to obtain diverse urban expansion data and assimilate multi-source spatiotemporal big data to supplement novel socio-economic driving factors; (3) to integrate with interpretable data-driven machine learning techniques to bolster the performance and reliability of DAUE models; and (4) to construct mechanism-coupled urban simulation to achieve a complementary enhancement and facilitate theory development and testing for urban land systems. [ABSTRACT FROM AUTHOR]