A spatial hierarchical learning module based cellular automata model for simulating urban expansion: case studies of three Chinese urban areas
Understanding the spatio-temporal evolution of urban expansion is essential for urban planning and sustainable development. Recently, cellular automata (CA)-based models have emerged as highly effective and widely utilized approaches for simulating urban expansion. However, they suffered from comple...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | GIScience & Remote Sensing |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/15481603.2023.2290352 |
| _version_ | 1826947429036982272 |
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| author | Xiaoyong Tan Min Deng Kaiqi Chen Yan Shi Bingbing Zhao Qinghao Liu |
| author_facet | Xiaoyong Tan Min Deng Kaiqi Chen Yan Shi Bingbing Zhao Qinghao Liu |
| author_sort | Xiaoyong Tan |
| collection | DOAJ |
| description | Understanding the spatio-temporal evolution of urban expansion is essential for urban planning and sustainable development. Recently, cellular automata (CA)-based models have emerged as highly effective and widely utilized approaches for simulating urban expansion. However, they suffered from complex structural information inherent in neighborhood effects, including spatio-temporal dimension disjunction and neighborhood sensitivity. To address these issues, herein, we propose a spatial hierarchical learning module based cellular automata model (SH-CA). Specifically, to tackle the spatio-temporal dimension disjunction, we take spatial dependence and historical expansion trends into consideration. We redefine the neighborhood structure and introduce lightweight convolutional neural networks to capture the complex spatio-temporal interaction in neighborhood effects. For the neighborhood sensitivity, we develop a gate filter to aggregate multiscale neighborhood effects for ensuring the synthesis of diverse neighborhood effects disparities. The proposed SH-CA model was implemented to simulate urban expansion in three distinct main urban areas of Beijing, Guangzhou, and Chengdu in China during 2010–2015. The results showed that the proposed SH-CA greatly improves the figure of merit and simulates the most real land-use patterns compared with other four sophisticated CA models. Moreover, the hierarchical learning module effectively modeled spatio-temporal interaction in neighborhood effects, mitigated neighborhood sensitivity, and showed a strong scalability to existing popular CA-based models. |
| first_indexed | 2024-03-09T01:03:32Z |
| format | Article |
| id | doaj.art-36420bf56f454b999128c8b87cce7c49 |
| institution | Directory Open Access Journal |
| issn | 1548-1603 1943-7226 |
| language | English |
| last_indexed | 2025-02-17T21:30:55Z |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj.art-36420bf56f454b999128c8b87cce7c492024-12-06T13:51:51ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262024-12-0161110.1080/15481603.2023.2290352A spatial hierarchical learning module based cellular automata model for simulating urban expansion: case studies of three Chinese urban areasXiaoyong Tan0Min Deng1Kaiqi Chen2Yan Shi3Bingbing Zhao4Qinghao Liu5Department of Geo-Informatics, School of Geosciences and Info-physics, Central South University, Changsha, ChinaDepartment of Geo-Informatics, School of Geosciences and Info-physics, Central South University, Changsha, ChinaDepartment of Geo-Informatics, School of Geosciences and Info-physics, Central South University, Changsha, ChinaDepartment of Geo-Informatics, School of Geosciences and Info-physics, Central South University, Changsha, ChinaDepartment of Geo-Informatics, School of Geosciences and Info-physics, Central South University, Changsha, ChinaDepartment of Geo-Informatics, School of Geosciences and Info-physics, Central South University, Changsha, ChinaUnderstanding the spatio-temporal evolution of urban expansion is essential for urban planning and sustainable development. Recently, cellular automata (CA)-based models have emerged as highly effective and widely utilized approaches for simulating urban expansion. However, they suffered from complex structural information inherent in neighborhood effects, including spatio-temporal dimension disjunction and neighborhood sensitivity. To address these issues, herein, we propose a spatial hierarchical learning module based cellular automata model (SH-CA). Specifically, to tackle the spatio-temporal dimension disjunction, we take spatial dependence and historical expansion trends into consideration. We redefine the neighborhood structure and introduce lightweight convolutional neural networks to capture the complex spatio-temporal interaction in neighborhood effects. For the neighborhood sensitivity, we develop a gate filter to aggregate multiscale neighborhood effects for ensuring the synthesis of diverse neighborhood effects disparities. The proposed SH-CA model was implemented to simulate urban expansion in three distinct main urban areas of Beijing, Guangzhou, and Chengdu in China during 2010–2015. The results showed that the proposed SH-CA greatly improves the figure of merit and simulates the most real land-use patterns compared with other four sophisticated CA models. Moreover, the hierarchical learning module effectively modeled spatio-temporal interaction in neighborhood effects, mitigated neighborhood sensitivity, and showed a strong scalability to existing popular CA-based models.https://www.tandfonline.com/doi/10.1080/15481603.2023.2290352Cellular automataurban expansionneighborhood effectshistorical expansion trendneighborhood sensitivityspatial hierarchical learning module |
| spellingShingle | Xiaoyong Tan Min Deng Kaiqi Chen Yan Shi Bingbing Zhao Qinghao Liu A spatial hierarchical learning module based cellular automata model for simulating urban expansion: case studies of three Chinese urban areas GIScience & Remote Sensing Cellular automata urban expansion neighborhood effects historical expansion trend neighborhood sensitivity spatial hierarchical learning module |
| title | A spatial hierarchical learning module based cellular automata model for simulating urban expansion: case studies of three Chinese urban areas |
| title_full | A spatial hierarchical learning module based cellular automata model for simulating urban expansion: case studies of three Chinese urban areas |
| title_fullStr | A spatial hierarchical learning module based cellular automata model for simulating urban expansion: case studies of three Chinese urban areas |
| title_full_unstemmed | A spatial hierarchical learning module based cellular automata model for simulating urban expansion: case studies of three Chinese urban areas |
| title_short | A spatial hierarchical learning module based cellular automata model for simulating urban expansion: case studies of three Chinese urban areas |
| title_sort | spatial hierarchical learning module based cellular automata model for simulating urban expansion case studies of three chinese urban areas |
| topic | Cellular automata urban expansion neighborhood effects historical expansion trend neighborhood sensitivity spatial hierarchical learning module |
| url | https://www.tandfonline.com/doi/10.1080/15481603.2023.2290352 |
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