Development Process, Quantitative Models, and Future Directions in Driving Analysis of Urban Expansion
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 su...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/12/4/174 |
| _version_ | 1797605237081505792 |
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| author | Xuefeng Guan Jingbo Li Changlan Yang Weiran Xing |
| author_facet | Xuefeng Guan Jingbo Li Changlan Yang Weiran Xing |
| author_sort | Xuefeng Guan |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T04:58:10Z |
| format | Article |
| id | doaj.art-84ce43b0fc0743c2bb0358e5c75e56b1 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-11T04:58:10Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-84ce43b0fc0743c2bb0358e5c75e56b12023-11-17T19:31:37ZengMDPI AGISPRS International Journal of Geo-Information2220-99642023-04-0112417410.3390/ijgi12040174Development Process, Quantitative Models, and Future Directions in Driving Analysis of Urban ExpansionXuefeng Guan0Jingbo Li1Changlan Yang2Weiran Xing3State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaSchool of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, ChinaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, ChinaDriving 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.https://www.mdpi.com/2220-9964/12/4/174urban expansiondriving mechanismcellular automataland urbanization |
| spellingShingle | Xuefeng Guan Jingbo Li Changlan Yang Weiran Xing Development Process, Quantitative Models, and Future Directions in Driving Analysis of Urban Expansion ISPRS International Journal of Geo-Information urban expansion driving mechanism cellular automata land urbanization |
| title | Development Process, Quantitative Models, and Future Directions in Driving Analysis of Urban Expansion |
| title_full | Development Process, Quantitative Models, and Future Directions in Driving Analysis of Urban Expansion |
| title_fullStr | Development Process, Quantitative Models, and Future Directions in Driving Analysis of Urban Expansion |
| title_full_unstemmed | Development Process, Quantitative Models, and Future Directions in Driving Analysis of Urban Expansion |
| title_short | Development Process, Quantitative Models, and Future Directions in Driving Analysis of Urban Expansion |
| title_sort | development process quantitative models and future directions in driving analysis of urban expansion |
| topic | urban expansion driving mechanism cellular automata land urbanization |
| url | https://www.mdpi.com/2220-9964/12/4/174 |
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