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

地理学报 ›› 2020, Vol. 75 ›› Issue (10): 2164-2179.doi: 10.11821/dlxb202010009

多层次矢量元胞自动机建模及土地利用变化模拟

孙毅中¹^,²^,³(), 杨静¹^,²^,³, 宋书颖¹^,²^,³, 朱杰⁴, 戴俊杰⁵

1.南京师范大学虚拟地理环境教育部重点实验室,南京 210023
2.江苏省地理环境演化国家重点实验室培育建设点,南京 210023
3.江苏省地理信息资源开发与利用协同创新中心,南京 210023
4.南京林业大学土木工程学院,南京 210037
5.江阴市城乡规划设计院,江阴 214433

收稿日期:2019-11-08 修回日期:2020-06-09 出版日期:2020-10-25 发布日期:2020-12-25
作者简介:孙毅中(1957-), 男, 江苏常州人, 教授, 博士生导师, 主要从事时空数据分析与城市规划GIS研究。E-mail: sunyizhong_cz@163.com
基金资助:
国家自然科学基金项目(41671392);国家自然科学基金项目(41871297)

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

SUN Yizhong¹^,²^,³(), YANG Jing¹^,²^,³, SONG Shuying¹^,²^,³, ZHU Jie⁴, DAI Junjie⁵

1. Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China
2. State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing 210023, China
3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
4. College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China
5. Jiangyin Urban and Rural Planning and Design Institute, Jiangyin 214433, Jiangsu, China

Received:2019-11-08 Revised:2020-06-09 Published:2020-10-25 Online:2020-12-25
Supported by:
National Natural Science Foundation of China(41671392);National Natural Science Foundation of China(41871297)

摘要/Abstract

摘要：

城市规划对土地利用变化起着重要的引导作用,各层次规划相互支撑、互为补充,自上而下影响着土地利用格局的演变。矢量元胞自动机以不规则的地理实体作为基本单元,可以更逼真地表达客观复杂的城市用地空间结构。然而,当面向具有层次协同性、空间引导性和管控传导性等特征的城市规划时,其元胞多层次体系构造、层次间协同方法和转换规则获取等关键问题凸显出来。本文以江阴市2007年、2012年、2017年3期土地利用现状数据为基础,在多层次矢量元胞自动机建模基础上,模拟了2017年土地利用变化,通过模拟结果与用地现状对比分析,对模型个别参数进行了修正,进一步提高了模型的可行性与适用性,进而预测了2022年城市土地利用格局。模拟结果显示,中心城片区建设用地发展已经趋于饱和,澄南、澄东南和澄东片区建设用地扩张较为明显,有逐步形成“中心城区—城镇组团—村庄”三级城乡空间聚落体系的趋势。最后利用FoM指标对模拟结果进行了评价,得到整体和各片区的精度基本都大于或接近于0.21,表明模拟结果精度较高,其构建的模型在面向多层次规划的用地变化模拟方面具有更好的效果。

关键词: 城市规划体系, 多层次矢量元胞自动机, 传导机制, 土地利用变化, 建模与模拟

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

孙毅中, 杨静, 宋书颖, 朱杰, 戴俊杰. 多层次矢量元胞自动机建模及土地利用变化模拟[J]. 地理学报, 2020, 75(10): 2164-2179.

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.

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参考文献 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

多层次矢量元胞自动机建模及土地利用变化模拟

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

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参考文献 30

相关文章 15

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	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

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[2]	张永强, 孔冬冬, 张选泽, 田静, 李聪聪. 2003—2017年植被变化对全球陆面蒸散发的影响[J]. 地理学报, 2021, 76(3): 584-594.
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[5]	刘纪远, 刘文超, 匡文慧, 宁佳. 基于主体功能区规划的中国城乡建设用地扩张时空特征遥感分析[J]. 地理学报, 2016, 71(3): 335-369.
[6]	李升发, 李秀彬. 耕地撂荒研究进展与展望[J]. 地理学报, 2016, 71(3): 370-389.
[7]	杨俊, 解鹏, 席建超, 葛全胜, 李雪铭, 马占东. 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[J]. 地理学报, 2015, 70(3): 461-475.
[8]	罗娅, 杨胜天, 刘晓燕, 刘昌明, 宋文龙, 董国涛, 赵海根, 娄和震. 黄河河口镇—潼关区间1998-2010年土地利用变化特征[J]. 地理学报, 2014, 69(1): 42-53.
[9]	吴琳娜, 杨胜天, 刘晓燕, 罗娅, 周旭, 赵海根. 1976年以来北洛河流域土地利用变化对人类活动程度的响应[J]. 地理学报, 2014, 69(1): 54-63.
[10]	刘纪远, 匡文慧, 张增祥, 徐新良, 秦元伟, 宁佳, 周万村, 张树文, 李仁东, 颜长珍, 吴世新, 史学正, 江南, 于东升, 潘贤章, 迟文峰. 20世纪80年代末以来中国土地利用变化的基本特征与空间格局[J]. 地理学报, 2014, 69(1): 3-14.
[11]	王渊刚, 罗格平, 赵树斌, 韩其飞, 李超凡, 范彬彬, 陈耀亮. 新疆耕地变化对区域碳平衡的影响[J]. 地理学报, 2014, 69(1): 110-120.
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[13]	杨依天, 郑度, 张雪芹, 刘羽. 1980-2010 年和田绿洲土地利用变化空间耦合及其环境效应[J]. 地理学报, 2013, 68(6): 813-824.
[14]	闫慧敏, 刘纪远, 黄河清, 董金玮, 徐新良, 王军邦. 城市化和退耕还林草对中国耕地生产力的影响[J]. 地理学报, 2012, 67(5): 579-588.
[15]	杜云艳1, 王丽敬2, 季民2, 曹峰1. 土地利用变化预测的案例推理方法[J]. 地理学报, 2009, 64(12): 1421-1429.