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...
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Autor*in:	Zhang, Yihan [verfasserIn] Liu, Xiaoping [verfasserIn] Chen, Guangliang [verfasserIn] Hu, Guohua [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Land use/land cover change (LUCC) Cellular automata (CA) Urban expansion Maximum entropy model

Übergeordnetes Werk:	Enthalten in: Science in China - Heidelberg : Springer, 1997, 63(2020), 5 vom: 03. Jan., Seite 701-712
Übergeordnetes Werk:	volume:63 ; year:2020 ; number:5 ; day:03 ; month:01 ; pages:701-712

Links:	Volltext

DOI / URN:	10.1007/s11430-019-9530-8

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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allfields	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
spelling	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
allfields_unstemmed	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
allfieldsGer	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
allfieldsSound	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
language	English
source	Enthalten in Science in China 63(2020), 5 vom: 03. Jan., Seite 701-712 volume:63 year:2020 number:5 day:03 month:01 pages:701-712
sourceStr	Enthalten in Science in China 63(2020), 5 vom: 03. Jan., Seite 701-712 volume:63 year:2020 number:5 day:03 month:01 pages:701-712
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Land use/land cover change (LUCC) Cellular automata (CA) Urban expansion Maximum entropy model
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container_title	Science in China
authorswithroles_txt_mv	Zhang, Yihan @@aut@@ Liu, Xiaoping @@aut@@ Chen, Guangliang @@aut@@ Hu, Guohua @@aut@@
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author	Zhang, Yihan
spellingShingle	Zhang, Yihan ddc 550 bkl 38.00 misc Land use/land cover change (LUCC) misc Cellular automata (CA) misc Urban expansion misc Maximum entropy model Simulation of urban expansion based on cellular automata and maximum entropy model
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topic_title	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
topic	ddc 550 bkl 38.00 misc Land use/land cover change (LUCC) misc Cellular automata (CA) misc Urban expansion misc Maximum entropy model
topic_unstemmed	ddc 550 bkl 38.00 misc Land use/land cover change (LUCC) misc Cellular automata (CA) misc Urban expansion misc Maximum entropy model
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title	Simulation of urban expansion based on cellular automata and maximum entropy model
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title_full	Simulation of urban expansion based on cellular automata and maximum entropy model
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author_browse	Zhang, Yihan Liu, Xiaoping Chen, Guangliang Hu, Guohua
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title_sort	simulation of urban expansion based on cellular automata and maximum entropy model
title_auth	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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title_short	Simulation of urban expansion based on cellular automata and maximum entropy model
url	https://dx.doi.org/10.1007/s11430-019-9530-8
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author2	Liu, Xiaoping Chen, Guangliang Hu, Guohua
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up_date	2024-07-03T23:47:49.931Z
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