Incorporation of spatial heterogeneity-weighted neighborhood into cellular automata for dynamic urban growth simulation
Cellular automata (CA) are bottom-up models that have been widely applied to simulate urban growth and project future urban scenarios. Conventional CA models commonly use a homogeneous neighborhood to represent the interactions among nearby cells, failing to reflect the spatial heterogeneity in land...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2019-10-01
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| Series: | GIScience & Remote Sensing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/15481603.2019.1603187 |
| _version_ | 1827811781366513664 |
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| author | Yongjiu Feng Xiaohua Tong |
| author_facet | Yongjiu Feng Xiaohua Tong |
| author_sort | Yongjiu Feng |
| collection | DOAJ |
| description | Cellular automata (CA) are bottom-up models that have been widely applied to simulate urban growth and project future urban scenarios. Conventional CA models commonly use a homogeneous neighborhood to represent the interactions among nearby cells, failing to reflect the spatial heterogeneity in landscapes. We develop new CA models for urban growth simulation by incorporating spatial heterogeneity into the neighborhood and constructing transition rules using genetic algorithms (GA). We employ three methods to quantify the spatial heterogeneity: [1] the land-use hotspot obtained using Getis-Ord Gi*, [2] the hotspot gradient (HOTGDT), and [3] the land-use gradient (LANDGDT). We compare the three methods and a homogeneous GA-CA model to simulate the rapid urban growth in Shaoxing, a small city in China. Our results show that, as compared to the homogeneous GA-CA model, the hotspot-based model produces unrealistically smooth urban patches and a lower figure of merit (FOM; lower by ~2.8%) while the two gradient-based models yield more realistic urban patches and higher FOMs (higher by ~6.4% for HOTGDT and ~4% for LANDGDT). The gradient-based methods substantially improve model performance and produce more justifiable urban patterns. We recommend including spatial heterogeneity in the CA neighborhood to represent the spatially nonstationary urban growth dynamics. The proposed gradient-based GA-CA models also show strong predictive ability in projecting future scenarios, which should help assess the impacts of past and current land-use policies, and the planning regulations on future urban development. |
| first_indexed | 2024-03-11T23:09:29Z |
| format | Article |
| id | doaj.art-73c6fbb5fe5f49fd80bd8ed6b3f4aff5 |
| institution | Directory Open Access Journal |
| issn | 1548-1603 1943-7226 |
| language | English |
| last_indexed | 2024-03-11T23:09:29Z |
| publishDate | 2019-10-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj.art-73c6fbb5fe5f49fd80bd8ed6b3f4aff52023-09-21T12:34:15ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262019-10-015671024104510.1080/15481603.2019.16031871603187Incorporation of spatial heterogeneity-weighted neighborhood into cellular automata for dynamic urban growth simulationYongjiu Feng0Xiaohua Tong1Tongji UniversityTongji UniversityCellular automata (CA) are bottom-up models that have been widely applied to simulate urban growth and project future urban scenarios. Conventional CA models commonly use a homogeneous neighborhood to represent the interactions among nearby cells, failing to reflect the spatial heterogeneity in landscapes. We develop new CA models for urban growth simulation by incorporating spatial heterogeneity into the neighborhood and constructing transition rules using genetic algorithms (GA). We employ three methods to quantify the spatial heterogeneity: [1] the land-use hotspot obtained using Getis-Ord Gi*, [2] the hotspot gradient (HOTGDT), and [3] the land-use gradient (LANDGDT). We compare the three methods and a homogeneous GA-CA model to simulate the rapid urban growth in Shaoxing, a small city in China. Our results show that, as compared to the homogeneous GA-CA model, the hotspot-based model produces unrealistically smooth urban patches and a lower figure of merit (FOM; lower by ~2.8%) while the two gradient-based models yield more realistic urban patches and higher FOMs (higher by ~6.4% for HOTGDT and ~4% for LANDGDT). The gradient-based methods substantially improve model performance and produce more justifiable urban patterns. We recommend including spatial heterogeneity in the CA neighborhood to represent the spatially nonstationary urban growth dynamics. The proposed gradient-based GA-CA models also show strong predictive ability in projecting future scenarios, which should help assess the impacts of past and current land-use policies, and the planning regulations on future urban development.http://dx.doi.org/10.1080/15481603.2019.1603187urban growth modelingspatial hotspotgetis-ord gi*gradientgenetic algorithmshaoxing city |
| spellingShingle | Yongjiu Feng Xiaohua Tong Incorporation of spatial heterogeneity-weighted neighborhood into cellular automata for dynamic urban growth simulation GIScience & Remote Sensing urban growth modeling spatial hotspot getis-ord gi* gradient genetic algorithm shaoxing city |
| title | Incorporation of spatial heterogeneity-weighted neighborhood into cellular automata for dynamic urban growth simulation |
| title_full | Incorporation of spatial heterogeneity-weighted neighborhood into cellular automata for dynamic urban growth simulation |
| title_fullStr | Incorporation of spatial heterogeneity-weighted neighborhood into cellular automata for dynamic urban growth simulation |
| title_full_unstemmed | Incorporation of spatial heterogeneity-weighted neighborhood into cellular automata for dynamic urban growth simulation |
| title_short | Incorporation of spatial heterogeneity-weighted neighborhood into cellular automata for dynamic urban growth simulation |
| title_sort | incorporation of spatial heterogeneity weighted neighborhood into cellular automata for dynamic urban growth simulation |
| topic | urban growth modeling spatial hotspot getis-ord gi* gradient genetic algorithm shaoxing city |
| url | http://dx.doi.org/10.1080/15481603.2019.1603187 |
| work_keys_str_mv | AT yongjiufeng incorporationofspatialheterogeneityweightedneighborhoodintocellularautomatafordynamicurbangrowthsimulation AT xiaohuatong incorporationofspatialheterogeneityweightedneighborhoodintocellularautomatafordynamicurbangrowthsimulation |