Simulation of urban expansion based on cellular automata and maximum entropy model
Abstract Urban expansion is a hot topic in land use/land cover change (LUCC) researches. In this paper, maximum entropy model and cellular automata (CA) model are coupled into a new CA model (Maxent-CA) for urban expansion. This model can help to obtain transition rules from single-period dataset. M...
Ausführliche Beschreibung
Autor*in: |
Zhang, Yihan [verfasserIn] Liu, Xiaoping [verfasserIn] Chen, Guangliang [verfasserIn] Hu, Guohua [verfasserIn] |
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Sprache: |
Englisch |
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2020 |
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Enthalten in: Science in China - Heidelberg : Springer, 1997, 63(2020), 5 vom: 03. Jan., Seite 701-712 |
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Übergeordnetes Werk: |
volume:63 ; year:2020 ; number:5 ; day:03 ; month:01 ; pages:701-712 |
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DOI / URN: |
10.1007/s11430-019-9530-8 |
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Katalog-ID: |
SPR039409678 |
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520 | |a Abstract Urban expansion is a hot topic in land use/land cover change (LUCC) researches. In this paper, maximum entropy model and cellular automata (CA) model are coupled into a new CA model (Maxent-CA) for urban expansion. This model can help to obtain transition rules from single-period dataset. Moreover, it can be constructed and calibrated easily with several steps. Firstly, Maxent-CA model was built by using remote sensing data of China in 2000 (basic data) and spatial variables (such as population density and Euclidean distance to cities). Secondly, the proposed model was calibrated by analyzing training samples, neighborhood structure and spatial scale. Finally, this model was verified by comparing logistic regression CA model and their simulation results. Experiments showed that suitable sampling ratio (sampling ratio equals the proportion of urban land in the whole region) and von Neumann neighborhood structure will help to yield better results. Spatial structure of simulation results becomes simple as spatial resolution decreases. Besides, simulation accuracy is significantly affected by spatial resolution. Compared to simulation results of logistic regression CA model, Maxent-CA model can avoid clusters phenomenon and obtain better results matching actual situation. It is found that the proposed model performs well in simulating urban expansion of China. It will be helpful for simulating even larger study area in the background of global environment changes. | ||
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700 | 1 | |a Hu, Guohua |e verfasserin |4 aut | |
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10.1007/s11430-019-9530-8 doi (DE-627)SPR039409678 (SPR)s11430-019-9530-8-e DE-627 ger DE-627 rakwb eng 550 ASE 38.00 bkl Zhang, Yihan verfasserin aut Simulation of urban expansion based on cellular automata and maximum entropy model 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Urban expansion is a hot topic in land use/land cover change (LUCC) researches. In this paper, maximum entropy model and cellular automata (CA) model are coupled into a new CA model (Maxent-CA) for urban expansion. This model can help to obtain transition rules from single-period dataset. Moreover, it can be constructed and calibrated easily with several steps. Firstly, Maxent-CA model was built by using remote sensing data of China in 2000 (basic data) and spatial variables (such as population density and Euclidean distance to cities). Secondly, the proposed model was calibrated by analyzing training samples, neighborhood structure and spatial scale. Finally, this model was verified by comparing logistic regression CA model and their simulation results. Experiments showed that suitable sampling ratio (sampling ratio equals the proportion of urban land in the whole region) and von Neumann neighborhood structure will help to yield better results. Spatial structure of simulation results becomes simple as spatial resolution decreases. Besides, simulation accuracy is significantly affected by spatial resolution. Compared to simulation results of logistic regression CA model, Maxent-CA model can avoid clusters phenomenon and obtain better results matching actual situation. It is found that the proposed model performs well in simulating urban expansion of China. It will be helpful for simulating even larger study area in the background of global environment changes. Land use/land cover change (LUCC) (dpeaa)DE-He213 Cellular automata (CA) (dpeaa)DE-He213 Urban expansion (dpeaa)DE-He213 Maximum entropy model (dpeaa)DE-He213 Liu, Xiaoping verfasserin aut Chen, Guangliang verfasserin aut Hu, Guohua verfasserin aut Enthalten in Science in China Heidelberg : Springer, 1997 63(2020), 5 vom: 03. Jan., Seite 701-712 (DE-627)385614748 (DE-600)2142896-7 1862-2801 nnns volume:63 year:2020 number:5 day:03 month:01 pages:701-712 https://dx.doi.org/10.1007/s11430-019-9530-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 38.00 ASE AR 63 2020 5 03 01 701-712 |
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10.1007/s11430-019-9530-8 doi (DE-627)SPR039409678 (SPR)s11430-019-9530-8-e DE-627 ger DE-627 rakwb eng 550 ASE 38.00 bkl Zhang, Yihan verfasserin aut Simulation of urban expansion based on cellular automata and maximum entropy model 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Urban expansion is a hot topic in land use/land cover change (LUCC) researches. In this paper, maximum entropy model and cellular automata (CA) model are coupled into a new CA model (Maxent-CA) for urban expansion. This model can help to obtain transition rules from single-period dataset. Moreover, it can be constructed and calibrated easily with several steps. Firstly, Maxent-CA model was built by using remote sensing data of China in 2000 (basic data) and spatial variables (such as population density and Euclidean distance to cities). Secondly, the proposed model was calibrated by analyzing training samples, neighborhood structure and spatial scale. Finally, this model was verified by comparing logistic regression CA model and their simulation results. Experiments showed that suitable sampling ratio (sampling ratio equals the proportion of urban land in the whole region) and von Neumann neighborhood structure will help to yield better results. Spatial structure of simulation results becomes simple as spatial resolution decreases. Besides, simulation accuracy is significantly affected by spatial resolution. Compared to simulation results of logistic regression CA model, Maxent-CA model can avoid clusters phenomenon and obtain better results matching actual situation. It is found that the proposed model performs well in simulating urban expansion of China. It will be helpful for simulating even larger study area in the background of global environment changes. Land use/land cover change (LUCC) (dpeaa)DE-He213 Cellular automata (CA) (dpeaa)DE-He213 Urban expansion (dpeaa)DE-He213 Maximum entropy model (dpeaa)DE-He213 Liu, Xiaoping verfasserin aut Chen, Guangliang verfasserin aut Hu, Guohua verfasserin aut Enthalten in Science in China Heidelberg : Springer, 1997 63(2020), 5 vom: 03. Jan., Seite 701-712 (DE-627)385614748 (DE-600)2142896-7 1862-2801 nnns volume:63 year:2020 number:5 day:03 month:01 pages:701-712 https://dx.doi.org/10.1007/s11430-019-9530-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 38.00 ASE AR 63 2020 5 03 01 701-712 |
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10.1007/s11430-019-9530-8 doi (DE-627)SPR039409678 (SPR)s11430-019-9530-8-e DE-627 ger DE-627 rakwb eng 550 ASE 38.00 bkl Zhang, Yihan verfasserin aut Simulation of urban expansion based on cellular automata and maximum entropy model 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Urban expansion is a hot topic in land use/land cover change (LUCC) researches. In this paper, maximum entropy model and cellular automata (CA) model are coupled into a new CA model (Maxent-CA) for urban expansion. This model can help to obtain transition rules from single-period dataset. Moreover, it can be constructed and calibrated easily with several steps. Firstly, Maxent-CA model was built by using remote sensing data of China in 2000 (basic data) and spatial variables (such as population density and Euclidean distance to cities). Secondly, the proposed model was calibrated by analyzing training samples, neighborhood structure and spatial scale. Finally, this model was verified by comparing logistic regression CA model and their simulation results. Experiments showed that suitable sampling ratio (sampling ratio equals the proportion of urban land in the whole region) and von Neumann neighborhood structure will help to yield better results. Spatial structure of simulation results becomes simple as spatial resolution decreases. Besides, simulation accuracy is significantly affected by spatial resolution. Compared to simulation results of logistic regression CA model, Maxent-CA model can avoid clusters phenomenon and obtain better results matching actual situation. It is found that the proposed model performs well in simulating urban expansion of China. It will be helpful for simulating even larger study area in the background of global environment changes. Land use/land cover change (LUCC) (dpeaa)DE-He213 Cellular automata (CA) (dpeaa)DE-He213 Urban expansion (dpeaa)DE-He213 Maximum entropy model (dpeaa)DE-He213 Liu, Xiaoping verfasserin aut Chen, Guangliang verfasserin aut Hu, Guohua verfasserin aut Enthalten in Science in China Heidelberg : Springer, 1997 63(2020), 5 vom: 03. Jan., Seite 701-712 (DE-627)385614748 (DE-600)2142896-7 1862-2801 nnns volume:63 year:2020 number:5 day:03 month:01 pages:701-712 https://dx.doi.org/10.1007/s11430-019-9530-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 38.00 ASE AR 63 2020 5 03 01 701-712 |
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10.1007/s11430-019-9530-8 doi (DE-627)SPR039409678 (SPR)s11430-019-9530-8-e DE-627 ger DE-627 rakwb eng 550 ASE 38.00 bkl Zhang, Yihan verfasserin aut Simulation of urban expansion based on cellular automata and maximum entropy model 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Urban expansion is a hot topic in land use/land cover change (LUCC) researches. In this paper, maximum entropy model and cellular automata (CA) model are coupled into a new CA model (Maxent-CA) for urban expansion. This model can help to obtain transition rules from single-period dataset. Moreover, it can be constructed and calibrated easily with several steps. Firstly, Maxent-CA model was built by using remote sensing data of China in 2000 (basic data) and spatial variables (such as population density and Euclidean distance to cities). Secondly, the proposed model was calibrated by analyzing training samples, neighborhood structure and spatial scale. Finally, this model was verified by comparing logistic regression CA model and their simulation results. Experiments showed that suitable sampling ratio (sampling ratio equals the proportion of urban land in the whole region) and von Neumann neighborhood structure will help to yield better results. Spatial structure of simulation results becomes simple as spatial resolution decreases. Besides, simulation accuracy is significantly affected by spatial resolution. Compared to simulation results of logistic regression CA model, Maxent-CA model can avoid clusters phenomenon and obtain better results matching actual situation. It is found that the proposed model performs well in simulating urban expansion of China. It will be helpful for simulating even larger study area in the background of global environment changes. Land use/land cover change (LUCC) (dpeaa)DE-He213 Cellular automata (CA) (dpeaa)DE-He213 Urban expansion (dpeaa)DE-He213 Maximum entropy model (dpeaa)DE-He213 Liu, Xiaoping verfasserin aut Chen, Guangliang verfasserin aut Hu, Guohua verfasserin aut Enthalten in Science in China Heidelberg : Springer, 1997 63(2020), 5 vom: 03. Jan., Seite 701-712 (DE-627)385614748 (DE-600)2142896-7 1862-2801 nnns volume:63 year:2020 number:5 day:03 month:01 pages:701-712 https://dx.doi.org/10.1007/s11430-019-9530-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 38.00 ASE AR 63 2020 5 03 01 701-712 |
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10.1007/s11430-019-9530-8 doi (DE-627)SPR039409678 (SPR)s11430-019-9530-8-e DE-627 ger DE-627 rakwb eng 550 ASE 38.00 bkl Zhang, Yihan verfasserin aut Simulation of urban expansion based on cellular automata and maximum entropy model 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Urban expansion is a hot topic in land use/land cover change (LUCC) researches. In this paper, maximum entropy model and cellular automata (CA) model are coupled into a new CA model (Maxent-CA) for urban expansion. This model can help to obtain transition rules from single-period dataset. Moreover, it can be constructed and calibrated easily with several steps. Firstly, Maxent-CA model was built by using remote sensing data of China in 2000 (basic data) and spatial variables (such as population density and Euclidean distance to cities). Secondly, the proposed model was calibrated by analyzing training samples, neighborhood structure and spatial scale. Finally, this model was verified by comparing logistic regression CA model and their simulation results. Experiments showed that suitable sampling ratio (sampling ratio equals the proportion of urban land in the whole region) and von Neumann neighborhood structure will help to yield better results. Spatial structure of simulation results becomes simple as spatial resolution decreases. Besides, simulation accuracy is significantly affected by spatial resolution. Compared to simulation results of logistic regression CA model, Maxent-CA model can avoid clusters phenomenon and obtain better results matching actual situation. It is found that the proposed model performs well in simulating urban expansion of China. It will be helpful for simulating even larger study area in the background of global environment changes. Land use/land cover change (LUCC) (dpeaa)DE-He213 Cellular automata (CA) (dpeaa)DE-He213 Urban expansion (dpeaa)DE-He213 Maximum entropy model (dpeaa)DE-He213 Liu, Xiaoping verfasserin aut Chen, Guangliang verfasserin aut Hu, Guohua verfasserin aut Enthalten in Science in China Heidelberg : Springer, 1997 63(2020), 5 vom: 03. Jan., Seite 701-712 (DE-627)385614748 (DE-600)2142896-7 1862-2801 nnns volume:63 year:2020 number:5 day:03 month:01 pages:701-712 https://dx.doi.org/10.1007/s11430-019-9530-8 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 38.00 ASE AR 63 2020 5 03 01 701-712 |
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In this paper, maximum entropy model and cellular automata (CA) model are coupled into a new CA model (Maxent-CA) for urban expansion. This model can help to obtain transition rules from single-period dataset. Moreover, it can be constructed and calibrated easily with several steps. Firstly, Maxent-CA model was built by using remote sensing data of China in 2000 (basic data) and spatial variables (such as population density and Euclidean distance to cities). Secondly, the proposed model was calibrated by analyzing training samples, neighborhood structure and spatial scale. Finally, this model was verified by comparing logistic regression CA model and their simulation results. Experiments showed that suitable sampling ratio (sampling ratio equals the proportion of urban land in the whole region) and von Neumann neighborhood structure will help to yield better results. Spatial structure of simulation results becomes simple as spatial resolution decreases. Besides, simulation accuracy is significantly affected by spatial resolution. Compared to simulation results of logistic regression CA model, Maxent-CA model can avoid clusters phenomenon and obtain better results matching actual situation. It is found that the proposed model performs well in simulating urban expansion of China. 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550 ASE 38.00 bkl Simulation of urban expansion based on cellular automata and maximum entropy model Land use/land cover change (LUCC) (dpeaa)DE-He213 Cellular automata (CA) (dpeaa)DE-He213 Urban expansion (dpeaa)DE-He213 Maximum entropy model (dpeaa)DE-He213 |
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simulation of urban expansion based on cellular automata and maximum entropy model |
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Simulation of urban expansion based on cellular automata and maximum entropy model |
abstract |
Abstract Urban expansion is a hot topic in land use/land cover change (LUCC) researches. In this paper, maximum entropy model and cellular automata (CA) model are coupled into a new CA model (Maxent-CA) for urban expansion. This model can help to obtain transition rules from single-period dataset. Moreover, it can be constructed and calibrated easily with several steps. Firstly, Maxent-CA model was built by using remote sensing data of China in 2000 (basic data) and spatial variables (such as population density and Euclidean distance to cities). Secondly, the proposed model was calibrated by analyzing training samples, neighborhood structure and spatial scale. Finally, this model was verified by comparing logistic regression CA model and their simulation results. Experiments showed that suitable sampling ratio (sampling ratio equals the proportion of urban land in the whole region) and von Neumann neighborhood structure will help to yield better results. Spatial structure of simulation results becomes simple as spatial resolution decreases. Besides, simulation accuracy is significantly affected by spatial resolution. Compared to simulation results of logistic regression CA model, Maxent-CA model can avoid clusters phenomenon and obtain better results matching actual situation. It is found that the proposed model performs well in simulating urban expansion of China. It will be helpful for simulating even larger study area in the background of global environment changes. |
abstractGer |
Abstract Urban expansion is a hot topic in land use/land cover change (LUCC) researches. In this paper, maximum entropy model and cellular automata (CA) model are coupled into a new CA model (Maxent-CA) for urban expansion. This model can help to obtain transition rules from single-period dataset. Moreover, it can be constructed and calibrated easily with several steps. Firstly, Maxent-CA model was built by using remote sensing data of China in 2000 (basic data) and spatial variables (such as population density and Euclidean distance to cities). Secondly, the proposed model was calibrated by analyzing training samples, neighborhood structure and spatial scale. Finally, this model was verified by comparing logistic regression CA model and their simulation results. Experiments showed that suitable sampling ratio (sampling ratio equals the proportion of urban land in the whole region) and von Neumann neighborhood structure will help to yield better results. Spatial structure of simulation results becomes simple as spatial resolution decreases. Besides, simulation accuracy is significantly affected by spatial resolution. Compared to simulation results of logistic regression CA model, Maxent-CA model can avoid clusters phenomenon and obtain better results matching actual situation. It is found that the proposed model performs well in simulating urban expansion of China. It will be helpful for simulating even larger study area in the background of global environment changes. |
abstract_unstemmed |
Abstract Urban expansion is a hot topic in land use/land cover change (LUCC) researches. In this paper, maximum entropy model and cellular automata (CA) model are coupled into a new CA model (Maxent-CA) for urban expansion. This model can help to obtain transition rules from single-period dataset. Moreover, it can be constructed and calibrated easily with several steps. Firstly, Maxent-CA model was built by using remote sensing data of China in 2000 (basic data) and spatial variables (such as population density and Euclidean distance to cities). Secondly, the proposed model was calibrated by analyzing training samples, neighborhood structure and spatial scale. Finally, this model was verified by comparing logistic regression CA model and their simulation results. Experiments showed that suitable sampling ratio (sampling ratio equals the proportion of urban land in the whole region) and von Neumann neighborhood structure will help to yield better results. Spatial structure of simulation results becomes simple as spatial resolution decreases. Besides, simulation accuracy is significantly affected by spatial resolution. Compared to simulation results of logistic regression CA model, Maxent-CA model can avoid clusters phenomenon and obtain better results matching actual situation. It is found that the proposed model performs well in simulating urban expansion of China. It will be helpful for simulating even larger study area in the background of global environment changes. |
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