多层次矢量元胞自动机建模及土地利用变化模拟

doi:10.11821/dlxb202010009

Abstract

Abstract:

Urban planning plays an important guiding role in land use change. The planning at all levels supports and complements each other, and influences the evolution of land-use pattern from top to bottom. Vector cellular automata, which takes irregular geographical entities as its basic units, can express the objective and complicated urban land use structure in a more realistic way, and has become one of the focus in the land use change research. However, when we face urban planning with the characteristics of hierarchical synergy, spatial guidance and controlled conductivity, some key issues are highlighted, such as the establishment of CA hierarchical system, the construction of hierarchical synergy and the acquisition of transition rules. After constructing the multilevel vector CA model, we chose Jiangyin City as the study area and its land use data covering the years 2007, 2012 and 2017 as the basic data to simulate the land use changes from 2012 to 2017. On the basis of multilevel vector cellular automata model, through the comparative analysis between the simulation results and the current situation of land use in 2017, the individual parameters of the model were modified to further improve the feasibility and applicability of the model, and then the urban land use pattern in 2022 was predicted. The simulation results indicated that the development of construction land in Central District has become saturated, while the expansions of construction land in Chengnan District, Chengdongnan District and Chengdong District were obvious. From an overall perspective, there was a tendency to gradually form a three-level urban-rural spatial settlement system "central city area-urban agglomeration-village". Using FoM index, we concluded that the simulation results had great simulation precision and the model had high feasibility and applicability, because the FoM values of the whole city and each district were almost greater than or close to 0.21. This shows that the simulation results are more accurate and the model is more effective in the simulation of land use change based on urban planning.

Key words: urban planning system, multilevel vector cellular automata, transmission mechanism, land use change, modelling and simulation

SUN Yizhong, YANG Jing, SONG Shuying, ZHU Jie, DAI Junjie. Modeling of multilevel vector cellular automata and its simulation of land use change[J].Acta Geographica Sinica, 2020, 75(10): 2164-2179.

Figures/Tables 16

Fig. 1

Tab. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Tab. 2

Tab. 3

Tab. 4

Tab. 5

Fig. 6

Fig. 7

Tab. 6

Tab. 7

Fig. 8

Fig. 9

References 30

[1]	Tang Huajun, Wu Wenbin, Yang Peng , et al. Recent progresses of land use and land cover change (LUCC) models. Acta Geographica Sinica, 2009,64(4):456-468.
	[ 唐华俊, 吴文斌, 杨鹏 , 等. 土地利用/土地覆被变化(LUCC)模型研究进展. 地理学报, 2009,64(4):456-468.]
[2]	Batty M, Xie Y . From cells to cities. Environment and Planning B: Urban Analytics and City Science, 1994,21(7):31-48.
[3]	Liao Jiangfu, Tang Lina, Wang Cuiping , et al. Measuring and calibrating extended neighborhood effect of urban cellular automata model based on particle swarm optimization. Progress in Geography, 2014,33(12):1624-1633.
	[ 廖江福, 唐立娜, 王翠平 , 等. 城市元胞自动机扩展邻域效应的测量与校准研究. 地理科学进展, 2014,33(12):1624-1633.]
[4]	Huang Jinchuan, Lin Haoxi . Spatial evolution analysis and multi-scenarios simulation of Beijing-Tianjin-Hebei Urban Agglomeration. Geographical Research, 2017,36(3):506-517.
	[ 黄金川, 林浩曦 . 京津冀城市群多情景空间演化模拟. 地理研究, 2017,36(3):506-517.]
[5]	Chen Baofen, Zhang Yaomin, Jiang Dong . Urban land expansion in Fuzhou City based on coupled cellular automata and agent-based models (CA-ABM). Progress in Geography, 2017,36(5):626-634.
	[ 陈宝芬, 张耀民, 江东 . 基于CA-ABM模型的福州城市用地扩张研究. 地理科学进展, 2017,36(5):626-634.]
[6]	Jiao Limin, Yang Ludi, Liu Jiafeng , et al. Construction and application of cellular automata model based on urban spatial structure information. Scientia Geographica Sinica, 2019,39(8):1276-1283.
	[ 焦利民, 杨璐迪, 刘稼丰 , 等. 顾及城市空间结构信息的元胞自动机模型构建及其应用. 地理科学, 2019,39(8):1276-1283.]
[7]	Yang Jun, Xie Peng, Xi Jianchao , et al. LUCC simulation based on the cellular automata simulation: A case study of Dalian Economic and Technological Development Zone. Acta Geographica Sinica, 2015,70(3):461-475.
	[ 杨俊, 解鹏, 席建超 , 等. 基于元胞自动机模型的土地利用变化模拟: 以大连经济技术开发区为例. 地理学报, 2015,70(3):461-475.]
[8]	Feng Yongjiu, Liu Miaolong, Tong Xiaohua , et al. Kernel principal components analysis based cellular model for restructuring and predicting urban evolution. Acta Geographica Sinica, 2010,65(6):665-675.
	[ 冯永玖, 刘妙龙, 童小华 , 等. 基于核主成分元胞模型的城市演化重建与预测. 地理学报, 2010,65(6):665-675.]
[9]	Long Ying, Shen Zhenjiang, Mao Qizhi , et al. Form scenario analysis using constrained Cellular Automata. Acta Geographica Sinica, 2010,65(6):643-655.
	[ 龙瀛, 沈振江, 毛其智 , 等. 基于约束性CA方法的北京城市形态情景分析. 地理学报, 2010,65(6):643-655.]
[10]	Long Ying, Han Haoying, Mao Qizhi . Establishing urban growth boundaries using constrained CA. Acta Geographica Sinica, 2009,64(8):999-1008.
	[ 龙瀛, 韩昊英, 毛其智 . 利用约束性CA制定城市增长边界. 地理学报, 2009,64(8):999-1008.]
[11]	Zhao Li, Yang Jun, Li Chuang , et al. Geographic cellular automata model research progress. Scientia Geographica Sinica, 2016,36(8):1190-1196.
	[ 赵莉, 杨俊, 李闯 , 等. 地理元胞自动机模型研究进展. 地理科学, 2016,36(8):1190-1196.]
[12]	Stevens D, Dragicevic S, Rothley K . iCity: A GIS-CA modelling tool for urban planning and decision making. Environmental Modelling & Software, 2007,22(6):761-773.
[13]	Lu Y, Cao M, Zhang L . A vector-based cellular automata model for simulating urban land use change. Chinese Geographical Science, 2015,25(1):74-84.
[14]	Wu Tinghai . A discussion on urban planning in the territorial and spatial planning system. City Planning Review, 2019,43(8):9-17.
	[ 武廷海 . 国土空间规划体系中的城市规划初论. 城市规划, 2019,43(8):9-17.]
[15]	Moreno N, Wang F, Marceau D J . Implementation of a dynamic neighborhood in a land-use vector-based cellular automata model. Computers, Environment and Urban Systems, 2009,33(1):44-54.
[16]	Yao Y, Liu X, Li X , et al. Simulating urban land-use changes at a large scale by integrating dynamic land parcel subdivision and vector-based cellular automata. International Journal of Geographical Information Science, 2017,31(12):2452-2479.
[17]	Pinto N N, Antunes A P . A cellular automata model based on irregular cells: Application to small urban areas. Environment and Planning B: Planning and Design, 2010,37(6):1095-1114.
[18]	Long Y, Shen Y, Jin X . Mapping block-level urban areas for all Chinesecities. Annals of the Association of American Geographers, 2015,106(1):96-113.
[19]	Dahal K R, Chow T E . Characterization of neighborhood sensitivity of an irregular cellular automata model of urban growth. International Journal of Geographical Information Science, 2015,29(3):475-497.
[20]	Dahal K R, Chow T E . An agent-integrated irregular automata model of urban land-use dynamics. International Journal of Geographical Information Science, 2014,28(11):2281-2303.
[21]	Benenson I, Omer I, Hatna E . Entity-based modeling of urban residential dynamics: The case of Yaffo, Tel Aviv. Environment and Planning B: Planning and Design, 2002,29:491-512.
[22]	Chen Chen, Zhao Min . Discussions on urban non-development land and its planning control. Urban Planning Forum, 2011(4):39-45.
	[ 陈晨, 赵民 . 对“非城市建设用地”及其规划管控问题的若干探讨. 城市规划学刊, 2011(4):39-45.]
[23]	Aburas M M, Ho Y M, Ramli M F , et al. The simulation and prediction of spatio-temporal urban growth trends using cellular automata models: A review. International Journal of Applied Earth Observation and Geoinformation, 2016,52:380-389.
[24]	Ghosh P, Mukhopadhyay A, Chanda A , et al. Application of cellular automata and Markov-chain model in geospatial environmental modeling: A review. Remote Sensing Applications: Society and Environment, 2017,5:64-77.
[25]	Shi W, Pang M Y C . Development of Voronoi-based cellular automata: An integrated dynamic model for geographical information systems. International Journal of Geographical Information Science, 2000,14(5):455-474.
[26]	SemboloniFerdinando. The growth of an urban cluster into a dynamic self-modifying spatial pattern. Environment and Planning B: Planning and Design, 2000,27(4):549-564.
[27]	Shahbazian Z, Faramarzi M, Rostami N , et al. Integrating logistic regression and cellular automata-Markov models with the experts' perceptions for detecting and simulating land use changes and their driving forces. Environmental Monitoring and Assessment, Environmental Monitoring and Assessment, 2019,191(7):422. doi: 10.1007/s10661-019-7555-4 pmid: 31177407
[28]	Arsanjani J J, Helbich M, Kainz W , et al. Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. International Journal of Applied Earth Observation and Geoinformation, 2012,21:265-275.
[29]	Ma Shifa, Ai Bin, Ou Jinpei . Spatial allocation of construction land with constrained cellular automata model. Scientia Geographica Sinica, 2013,33(10):1245-1251.
	[ 马世发, 艾彬, 欧金沛 . 约束性CA在城乡建设用地指标空间化中的应用. 地理科学, 2013,33(10):1245-1251.]
[30]	Pontius R G, Boersma W, Castella J C , et al. Comparing the input, output, and validation maps for several models of land change. The Annals of Regional Science, 2008,42(1):11-37.

	用地类型
	农用地	山地	村庄建设用地	城镇建设用地	道路用地	水域	特殊用地
编码	1	2	3	4	5	6	7

		2007年用地类型
		1	2	3	4
2012年用地类型	1	0.7244	0.2263	0.2017	0.0946
	2	0.0156	0.5439	0.0009	0.009
	3	0.0592	0.0179	0.6148	0.0704
	4	0.2008	0.2119	0.1825	0.8251
	合计	1	1	0.9999	0.9991

道路	距离(m)	分级道路影响参数			最显著影响范围(m)	分级道路影响参数
道路	距离(m)	回归系数	回归常数	卡方值	最显著影响范围(m)	回归系数	回归常数
高速路	250	2.407	-4.459	2.407	1000	-1.668	2.879
	500	0.002	-0.040	0.002
	750	4.977	-1.076	4.977
	1000	4.981	-0.676	4.981
快速路	250	-5.174	-1.996	2.228	500	-1.346
	500	-1.346	-2.413	6.241
	750	-0.926	-2.490	5.074
	1000	-0.609	-2.575	4.569
干线公路	250	-1.668	-2.922	1.663	750	-4.459
	500	-1.136	-2.975	0.262
	750	-0.523	-3.077	5.638
	1000	-0.086	-3.198	3.936

片区	用地类型	用地类型				片区	用地类型	用地类型
片区	用地类型	1	2	3	4	片区	用地类型	1	2	3	4
市域范围	1	0.7244	0.4961	0.5237	0.5413	中心片区	1	0.5305	0.5765	0.4053	0.3094
	2	0.0565	0.5439	0.0025	0.0557		2	0.1574	0.5764	0.0135	0.2155
	3	0.2149	0.0394	0.6148	0.4030		3	0.0874	0.0324	0.5682	0.4751
	4	0.7286	0.4646	0.4738	0.8251		4	0.7552	0.3911	0.5812	0.8887
澄西片区	1	0.7176	0.6332	0.3400	0.7916	澄东片区	1	0.6770	0.3333	0.4370	0.5457
	2	0.0023	0.2536	0.0005	0.0014		2	0.0178	0.6673	0.0037	0.0379
	3	0.1394	0.0309	0.5742	0.2070		3	0.2132	0.0473	0.6151	0.4164
	4	0.8584	0.3359	0.6595	0.8546		4	0.769	0.6193	0.5593	0.8119
澄南片区	1	0.8561	0.2582	0.7488	0.5366	澄东南片区	1	0.855	0.7424	0.5604	0.5303
	2	0.0103	0.1947	0	0.0203		2	0.0003	0.1852	0	0.0232
	3	0.3635	0.0329	0.7325	0.4431		3	0.3959	0	0.8172	0.4465
	4	0.6263	0.7089	0.2521	0.7834		4	0.6038	0.2576	0.4396	0.8064

	片区					市域范围
	中心片区	澄西片区	澄东片区	澄南片区	澄东南片区	市域范围
规划人口(万人)	130	17	46.5	25	26.5	245
建设用地指标(km²)	226.27	29.59	80.93	43.51	46.12	426.42

Modeling of multilevel vector cellular automata and its simulation of land use change

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 16

References 30

Related Articles 15

Metrics

Comments

	市域范围		中心城区		澄西片区		澄东片区		澄南片区		澄东南片区
	现状	模拟	现状	模拟	现状	模拟	现状	模拟	现状	模拟	现状	模拟
建设用地面积(km²)	419.45	425.37	227.19	226.27	28.47	29.59	80.84	80.93	42.19	43.51	40.76	45.07
FoM(%)	24.11		28.08		25.70		23.46		19.87		20.27
Kappa	0.82		0.86		0.81		0.84		0.82		0.82

	市域范围	中心城区	澄西片区	澄东片区	澄南片区	澄东南片区
2022年预测建设用地面积(km²)	431.87	227.45	30.96	82.65	45.45	45.36
2017年实际建设用地面积(km²)	419.45	227.19	28.47	80.84	42.19	40.76
预测增量(km²)	12.42	0.26	2.49	1.81	3.26	4.60
增长率(%)	2.96	0.11	8.74	2.24	7.73	11.29

[1]	SHENG Kerong, ZHANG Jie, ZHANG Hongxia. Network embedding and urban economic growth in China: A study based on the corporate networks of top 500 public companies [J]. Acta Geographica Sinica, 2021, 76(4): 818-834.
[2]	ZHANG Yongqiang, KONG Dongdong, ZHANG Xuanze, TIAN Jing, LI Congcong. Impacts of vegetation changes on global evapotranspiration in the period 2003-2017 [J]. Acta Geographica Sinica, 2021, 76(3): 584-594.
[3]	LIU Jiyuan,NING Jia,KUANG Wenhui,XU Xinliang,ZHANG Shuwen,YAN Changzhen,LI Rendong,WU Shixin,HU Yunfeng,DU Guoming,CHI Wenfeng,PAN Tao,NING Jing. Spatio-temporal patterns and characteristics of land-use change in China during 2010-2015 [J]. Acta Geographica Sinica, 2018, 73(5): 789-802.
[4]	Lijuan ZHANG, Ziyan YAO, Shihao TANG, Xiaxiang LI, Tiantian HAO. Spatiotemporal characteristics and patterns of the global cultivated land since the 1980s [J]. Acta Geographica Sinica, 2017, 72(7): 1235-1247.
[5]	Jiyuan LIU, Wenchao LIU, Wenhui KUANG, Jia NING. Remote Sensing-based Analysis of the Spatiotemporal Characteristics of Built-up area across China Based on the Plan for Major Function-oriented Zones [J]. Acta Geographica Sinica, 2016, 71(3): 335-369.
[6]	Huiyi ZHU, Minghui SUN. Main progress in the research on land use intensification [J]. Acta Geographica Sinica, 2014, 69(9): 1346-1357.
[7]	LUO Ya, YANG Shengtian, LIU Xiaoyan, LIU Changming, SONG Wenlong, DONG Guotao, ZHAO Haigen, LOU Hezhen. Land use change in the reach from Hekouzhen to Tongguan of the Yellow River during 1998-2010 [J]. Acta Geographica Sinica, 2014, 69(1): 42-53.
[8]	WU Linna, YANG Shengtian, LIU Xiaoyan, LUO Ya, ZHOU Xu, ZHAO Haigen. Response analysis of land use change to the degree of human activities in Beiluo River basin since 1976 [J]. Acta Geographica Sinica, 2014, 69(1): 54-63.
[9]	LIU Jiyuan, KUANG Wenhui, ZHANG Zengxiang, XU Xinliang, QIN Yuanwei, NING Jia, ZHOU Wancun, ZHANG Shuwen, LI Rendong, YAN Changzhen, WU Shixin, SHI Xuezheng, JIANG Nan, YU Dongsheng, PAN Xianzhang, CHI Wenfeng. Spatiotemporal characteristics, patterns and causes of land use changes in China since the late 1980s [J]. Acta Geographica Sinica, 2014, 69(1): 3-14.
[10]	ZHU Huiyi. Incentive of land use change：A case study on the variations of agricultural factor productivity in Xinjiang [J]. Acta Geographica Sinica, 2013, 68(8): 1029-1037.
[11]	YANG Yitian, ZHENG Du, ZHANG Xueqin, LIU Yu. The spatial coupling of land use changes and its environmental effects on Hotan oasis during 1980-2010 [J]. Acta Geographica Sinica, 2013, 68(6): 813-824.
[12]	MA Caihong, REN Zhiyuan, LI Xiaoyan. Land use change flow and its spatial agglomeration in the loess platform region [J]. Acta Geographica Sinica, 2013, 68(2): 257-267.
[13]	PANG Jiangli, ZHANG Weiqing, HUANG Chunchang, ZHA Xiaochun, ZHANG Caiyun, CHANG Meirong, ZHU Meiling, DING Min. The Influence of Land Use Change on Soil Development and over the Loess Tablelands in the Northern Weihe River Basin, China [J]. Acta Geographica Sinica, 2010, 65(7): 789-800.
[14]	DU Yun-Yan-1, Wang-Li-Jing-2, Ji-Min-2, Cao-Feng-1. A CBR Approach for Land Use Change Prediction [J]. Acta Geographica Sinica, 2009, 64(12): 1421-1429.
[15]	LIU Ji-Yuan-1, Zhang-Ceng-Xiang-2, Xu-Xin-Liang-1, Kuang-Wen-Hui-1, Zhou-Mo-Cun-3, Zhang-Shu-Wen-4, LI Ren-Dong-5, Ya-Chang-Zhen-6, Xu-Dong-Sheng-7, Tun-Shi-Xin-8, Jiang-Na-9. Spatial Patterns and Driving Forces of Land Use Change in China in the Early 21st Century [J]. Acta Geographica Sinica, 2009, 64(12): 1411-1420.