Modelling urban expansion with cellular automata supported by urban growth intensity over time
ABSTRACTThe simulation of urban expansion has become an important means to assist urban development planning and ecological sustainable development. However, the spatial and temporal heterogeneities of urban expansion has been a major challenge for modelling urban expansion. This study designed thre...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-07-01
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| Series: | Annals of GIS |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19475683.2023.2181393 |
| _version_ | 1827862191331606528 |
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| author | Jinqu Zhang Donglin Wu A-Xing Zhu Yunqiang Zhu |
| author_facet | Jinqu Zhang Donglin Wu A-Xing Zhu Yunqiang Zhu |
| author_sort | Jinqu Zhang |
| collection | DOAJ |
| description | ABSTRACTThe simulation of urban expansion has become an important means to assist urban development planning and ecological sustainable development. However, the spatial and temporal heterogeneities of urban expansion has been a major challenge for modelling urban expansion. This study designed three features from the perspective of spatiotemporal heterogeneity to improve the accuracy of CA model. The new features cover the trends effects of long time-series data on urban expansion, urban spatial growth intensity based on urban growth kernel estimation and allocation probability of the newly generated urban cells from global neighbourhood effects. Finally, urban expansion in Huizhou, China, was simulated and predicted. The experimental results show that the new features can effectively reduce the prediction error for the total amount of urban growth with a deviation of about 2%, and the overall accuracy of urban expansion is as high as 0.93. The features designed in this paper are shown to be effective and can be applied to urban simulations and scenario prediction with various models. |
| first_indexed | 2024-03-12T13:49:43Z |
| format | Article |
| id | doaj.art-23b2240513504a59b363c71dbffcafc2 |
| institution | Directory Open Access Journal |
| issn | 1947-5683 1947-5691 |
| language | English |
| last_indexed | 2024-03-12T13:49:43Z |
| publishDate | 2023-07-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Annals of GIS |
| spelling | doaj.art-23b2240513504a59b363c71dbffcafc22023-08-23T06:19:59ZengTaylor & Francis GroupAnnals of GIS1947-56831947-56912023-07-0129333735310.1080/19475683.2023.2181393Modelling urban expansion with cellular automata supported by urban growth intensity over timeJinqu Zhang0Donglin Wu1A-Xing Zhu2Yunqiang Zhu3School of Computer Science, South China Normal University, Guangzhou, ChinaSchool of Computer Science, South China Normal University, Guangzhou, ChinaCentre for Social Sciences, Southern University of Science and Technology, Shenzhen, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, ChinaABSTRACTThe simulation of urban expansion has become an important means to assist urban development planning and ecological sustainable development. However, the spatial and temporal heterogeneities of urban expansion has been a major challenge for modelling urban expansion. This study designed three features from the perspective of spatiotemporal heterogeneity to improve the accuracy of CA model. The new features cover the trends effects of long time-series data on urban expansion, urban spatial growth intensity based on urban growth kernel estimation and allocation probability of the newly generated urban cells from global neighbourhood effects. Finally, urban expansion in Huizhou, China, was simulated and predicted. The experimental results show that the new features can effectively reduce the prediction error for the total amount of urban growth with a deviation of about 2%, and the overall accuracy of urban expansion is as high as 0.93. The features designed in this paper are shown to be effective and can be applied to urban simulations and scenario prediction with various models.https://www.tandfonline.com/doi/10.1080/19475683.2023.2181393Cellular automatapiecewise regressionurban modellingspatial growth intensity |
| spellingShingle | Jinqu Zhang Donglin Wu A-Xing Zhu Yunqiang Zhu Modelling urban expansion with cellular automata supported by urban growth intensity over time Annals of GIS Cellular automata piecewise regression urban modelling spatial growth intensity |
| title | Modelling urban expansion with cellular automata supported by urban growth intensity over time |
| title_full | Modelling urban expansion with cellular automata supported by urban growth intensity over time |
| title_fullStr | Modelling urban expansion with cellular automata supported by urban growth intensity over time |
| title_full_unstemmed | Modelling urban expansion with cellular automata supported by urban growth intensity over time |
| title_short | Modelling urban expansion with cellular automata supported by urban growth intensity over time |
| title_sort | modelling urban expansion with cellular automata supported by urban growth intensity over time |
| topic | Cellular automata piecewise regression urban modelling spatial growth intensity |
| url | https://www.tandfonline.com/doi/10.1080/19475683.2023.2181393 |
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