Dynamic land use change simulation using cellular automata with spatially nonstationary transition rules
The dynamic relationships between land use change and its driving forces vary spatially and can be identified by geographically weighted regression (GWR). We present a novel cellular automata (GWR-CA) model that incorporates GWR-derived spatially varying relationships to simulate land use change. Ou...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2018-09-01
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| Series: | GIScience & Remote Sensing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/15481603.2018.1426262 |
| _version_ | 1797679066185203712 |
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| author | Yongjiu Feng Xiaohua Tong |
| author_facet | Yongjiu Feng Xiaohua Tong |
| author_sort | Yongjiu Feng |
| collection | DOAJ |
| description | The dynamic relationships between land use change and its driving forces vary spatially and can be identified by geographically weighted regression (GWR). We present a novel cellular automata (GWR-CA) model that incorporates GWR-derived spatially varying relationships to simulate land use change. Our GWR-CA model is characterized by spatially nonstationary transition rules that fully address local interactions in land use change. More importantly, each driving factor in our GWR model contains effects that both promote and resist land use change. We applied GWR-CA to simulate rapid land use change in Suzhou City on the Yangtze River Delta from 2000 to 2015. The GWR coefficients were visualized to highlight their spatial patterns and local variation, which are closely associated with their effects on land use change. The transition rules indicate low land conversion potential in the city’s center and outer suburbs, but higher land conversion potential in the inner near suburbs along the belt expressway. Residual statistics show that GWR fits the input data better than logistic regression (LR). Compared with an LR-based CA model, GWR-CA improves overall accuracy by 4.1% and captures 5.5% more urban growth, suggesting that GWR-CA may be superior in modeling land use change. Our results demonstrate that the GWR-CA model is effective in capturing spatially varying land transition rules to produce more realistic results, and is suitable for simulating land use change and urban expansion in rapidly urbanizing regions. |
| first_indexed | 2024-03-11T23:09:00Z |
| format | Article |
| id | doaj.art-01eb7c75e8c84c75a670c70fdb50745b |
| institution | Directory Open Access Journal |
| issn | 1548-1603 1943-7226 |
| language | English |
| last_indexed | 2024-03-11T23:09:00Z |
| publishDate | 2018-09-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj.art-01eb7c75e8c84c75a670c70fdb50745b2023-09-21T12:34:14ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262018-09-0155567869810.1080/15481603.2018.14262621426262Dynamic land use change simulation using cellular automata with spatially nonstationary transition rulesYongjiu Feng0Xiaohua Tong1Shanghai Ocean UniversityTongji UniversityThe dynamic relationships between land use change and its driving forces vary spatially and can be identified by geographically weighted regression (GWR). We present a novel cellular automata (GWR-CA) model that incorporates GWR-derived spatially varying relationships to simulate land use change. Our GWR-CA model is characterized by spatially nonstationary transition rules that fully address local interactions in land use change. More importantly, each driving factor in our GWR model contains effects that both promote and resist land use change. We applied GWR-CA to simulate rapid land use change in Suzhou City on the Yangtze River Delta from 2000 to 2015. The GWR coefficients were visualized to highlight their spatial patterns and local variation, which are closely associated with their effects on land use change. The transition rules indicate low land conversion potential in the city’s center and outer suburbs, but higher land conversion potential in the inner near suburbs along the belt expressway. Residual statistics show that GWR fits the input data better than logistic regression (LR). Compared with an LR-based CA model, GWR-CA improves overall accuracy by 4.1% and captures 5.5% more urban growth, suggesting that GWR-CA may be superior in modeling land use change. Our results demonstrate that the GWR-CA model is effective in capturing spatially varying land transition rules to produce more realistic results, and is suitable for simulating land use change and urban expansion in rapidly urbanizing regions.http://dx.doi.org/10.1080/15481603.2018.1426262land use changecellular automatageographically weighted regressionspatially varying transition rulessuzhou |
| spellingShingle | Yongjiu Feng Xiaohua Tong Dynamic land use change simulation using cellular automata with spatially nonstationary transition rules GIScience & Remote Sensing land use change cellular automata geographically weighted regression spatially varying transition rules suzhou |
| title | Dynamic land use change simulation using cellular automata with spatially nonstationary transition rules |
| title_full | Dynamic land use change simulation using cellular automata with spatially nonstationary transition rules |
| title_fullStr | Dynamic land use change simulation using cellular automata with spatially nonstationary transition rules |
| title_full_unstemmed | Dynamic land use change simulation using cellular automata with spatially nonstationary transition rules |
| title_short | Dynamic land use change simulation using cellular automata with spatially nonstationary transition rules |
| title_sort | dynamic land use change simulation using cellular automata with spatially nonstationary transition rules |
| topic | land use change cellular automata geographically weighted regression spatially varying transition rules suzhou |
| url | http://dx.doi.org/10.1080/15481603.2018.1426262 |
| work_keys_str_mv | AT yongjiufeng dynamiclandusechangesimulationusingcellularautomatawithspatiallynonstationarytransitionrules AT xiaohuatong dynamiclandusechangesimulationusingcellularautomatawithspatiallynonstationarytransitionrules |