基于近邻传播聚类元胞自动机模型的武汉城市扩散和聚合过程同步模拟

doi:10.11821/dlxb202110013

Abstract

Abstract:

Cellular automata (CA) has been widely recognized as an effective approach in the simulation of spatiotemporal dynamics of metropolitan areas, particularly for infilling and edge urban expansion processes. However, the traditional Logistics-CA has its severe drawbacks in simulating outlying expansion, since it evolves primarily according to the status of a set of neighboring cells, failing to identify other potential seeds that could also stimulate urban expansion in a significant way. This paper develops an advanced CA, called APCA, by using Affinity Propagation (AP) to comprehensively search for urban expansion seed points, as well as by realizing a synchronous simulation of diffusional and aggregational processes of urban cell. This paper uses the data of Wuhan, the largest provincial capital in central China, to validate the effectiveness of APCA. By simulating Wuhan's urban expansion dynamics between 1995 and 2025, the APCA (1) identifies that the total areas of outlying expansion amount to 8.67 km², accounting for 6.30% of added urban land of the city; and (2) successfully simulates a process of "first diffusion and then aggregation" in Wuhan, which is in harmony with the Phase Theory of urban expansion. Compared with the traditional Logistics-CA, the overall accuracy of APCA remains higher regardless of the number of seed points as long as they are within 1-8, while the accuracy of APCA reaches its highest (0.5217) when the seed point is set as 6. The APCA contributes to the two-dimensional CA framework by expanding surface-dimension simulation to point-dimension simulation, and thereby facilitates effective and accurate simulations of urban expansion patterns.

Key words: cellular automata, affinity propagation, diffusional urban expansion, Wuhan

HE Qingsong, TAN Ronghui, YANG Jun. Synchronized simulation of urban diffusional and aggregational process based on the affinity propagation cellular automata: A case study of Wuhan city[J].Acta Geographica Sinica, 2021, 76(10): 2522-2535.

Figures/Tables 9

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Tab. 1

Fig. 5

Fig. 6

Tab. 2

Fig. 7

References 31

[1]	Santé I, García A M, Miranda D, et al. Cellular automata models for the simulation of real-world urban processes: A review and analysis. Landscape and Urban Planning, 2010, 96(2):108-122. doi: 10.1016/j.landurbplan.2010.03.001
[2]	Feng Yongjiu, Yang Qianqian, Cui Li, et al. Simulation and prediction of urban land use change with spatial autoregressive model based cellular automata. Geography and Geo-Information Science, 2016, 32(5):37-44, 127.
	[ 冯永玖, 杨倩倩, 崔丽, 等. 基于空间自回归CA模型的城市土地利用变化模拟与预测. 地理与地理信息科学, 2016, 32(5):37-44, 127.]
[3]	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.]
[4]	Zhao Li, Yang Jun, Li Chuang, et al. Progress on geographic cellular automata model. Scientia Geographica Sinica, 2016, 36(8):1190-1196. doi: 10.13249/j.cnki.sgs.2016.08.009
	[ 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.]
[5]	Sun Yizhong, Yang Jing, Song Shuying, et al. Modeling of multilevel vector cellular automata and its simulation of land use change. Acta Geographica Sinica, 2020, 75(10):2164-2179. doi: 10.11821/dlxb202010009
	[ 孙毅中, 杨静, 宋书颖, 等. 多层次矢量元胞自动机建模及土地利用变化模拟. 地理学报, 2020, 75(10):2164-2179.]
[6]	Lu Y, Laffan S, Pettit C, et al. Land use change simulation and analysis using a vector cellular automata (CA) model: A case study of Ipswich City, Queensland, Australia. Environment and Planning B: Urban Analytics and City Science, 2020, 47(9):1605-1621. doi: 10.1177/2399808319830971
[7]	Qin Jing, Fang Chuanglin, Wang Yang. The three-dimensional urban growth simulating based on cellular automata. Journal of Geo-Information Science, 2013, 15(5):662-671. doi: 10.3724/SP.J.1047.2013.00662
	[ 秦静, 方创琳, 王洋. 基于元胞自动机的城市三维空间增长仿真模拟. 地球信息科学学报, 2013, 15(5):662-671.]
[8]	He Q S, Liu Y L, Zeng C, et al. Simultaneously simulate vertical and horizontal expansions of a future urban landscape: A case study in Wuhan, Central China. International Journal of Geographical Information Science, 2017, 31(10):1907-1928. doi: 10.1080/13658816.2017.1338707
[9]	Gao C, Feng Y J, Tong X H, et al. Modeling urban growth using spatially heterogeneous cellular automata models: Comparison of spatial lag, spatial error and GWR. Computers,Environment and Urban Systems, 2020, 81:101459. DOI: 10.1016/j.compenvurbsys.2020.101459. doi: 10.1016/j.compenvurbsys.2020.101459
[10]	Wang Haijun, Xia Chang, Zhang Anqi, et al. Calibrating urban expansion cellular automata using biogeography-based optimization. Geomatics and Information Science of Wuhan University, 2017, 42(9):1323-1329.
	[ 王海军, 夏畅, 张安琪, 等. 利用生物地理学优化算法获取城市扩展元胞自动机模型参数. 武汉大学学报·信息科学版, 2017, 42(9):1323-1329.]
[11]	Xie Zhiwen, Wang Haijun, Zhang Bin, et al. Urban expansion cellular automata model based on multi-structures convolutional neural networks. Acta Geodaetica et Cartographica Sinica, 2020, 49(3):375-385.
	[ 谢志文, 王海军, 张彬, 等. 城市扩展元胞自动机多结构卷积神经网络模型. 测绘学报, 2020, 49(3):375-385.]
[12]	Liu Xiaoping, Li Xia. Retrieving CA nonlinear transition rule from high-dimensional feature space. Acta Geographica Sinica, 2006, 61(6):663-672.
	[ 刘小平, 黎夏. 从高维特征空间中获取元胞自动机的非线性转换规则. 地理学报, 2006, 61(6):663-672.]
[13]	Liu Y L, He Q S, Tan R H, et al. Modeling different urban growth patterns based on the evolution of urban form: A case study from Huangpi, Central China. Applied Geography, 2016, 66:109-118. doi: 10.1016/j.apgeog.2015.11.012
[14]	Liu X P, Ma L, Li X, et al. Simulating urban growth by integrating landscape expansion index (LEI) and cellular automata. International Journal of Geographical Information Science, 2014, 28(1):148-163. doi: 10.1080/13658816.2013.831097
[15]	Liu Xiaoping, Li Xia, Chen Yimin, et al. Landscape expansion index and its applications to quantitative analysis of urban expansion. Acta Geographica Sinica, 2009, 64(12):1430-1438. doi: 10.11821/xb200912003
	[ 刘小平, 黎夏, 陈逸敏, 等. 景观扩张指数及其在城市扩展分析中的应用. 地理学报, 2009, 64(12):1430-1438.]
[16]	He Q S, Song Y, Liu Y L, et al. Diffusion or coalescence? Urban growth pattern and change in 363 Chinese cities from 1995 to 2015. Sustainable Cities and Society, 2017, 35:729-739. doi: 10.1016/j.scs.2017.08.033
[17]	Yu Yan, Hu Shanshan, Tong Yan, et al. Research on modeling for urban expansion pattern recognition based on shared boundary analysis. Geography and Geo-Information Science, 2017, 33(1):78-81, 101, 127.
	[ 俞艳, 胡珊珊, 童艳, 等. 基于公共边测度的城市扩张模式建模研究. 地理与地理信息科学, 2017, 33(1):78-81, 101, 127.]
[18]	Shi Y Q, Sun X, Zhu X D, et al. Characterizing growth types and analyzing growth density distribution in response to urban growth patterns in peri-urban areas of Lianyungang City. Landscape and Urban Planning, 2012, 105(4):425-433. doi: 10.1016/j.landurbplan.2012.01.017
[19]	Cheng Lan, Wu Zhifeng, Wei Jianbing, et al. Spatial identification of built-up land expansion and its significance. Chinese Journal of Ecology, 2009, 28(12):2593-2599.
	[ 程兰, 吴志峰, 魏建兵, 等. 城镇建设用地扩展类型的空间识别及其意义. 生态学杂志, 2009, 28(12):2593-2599.]
[20]	Clarke K C, Hoppen S, Gaydos L. A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning B: Planning and Design, 1997, 24(2):247-261. doi: 10.1068/b240247
[21]	Frey B J, Dueck D. Clustering by passing messages between data points. Science, 2007, 315(5814):972-976. doi: 10.1126/science.1136800
[22]	Liu Y L, Tang M Y, Wu Z H, et al. Analysis of passenger flow characteristics and their relationship with surrounding urban functional landscape pattern. Transactions in GIS, 2020, 24(6):1602-1629. doi: 10.1111/tgis.v24.6
[23]	Feng Y J, Liu Y, Tong X H. Comparison of metaheuristic cellular automata models: A case study of dynamic land use simulation in the Yangtze River Delta. Computers, Environment and Urban Systems, 2018, 70:138-150. doi: 10.1016/j.compenvurbsys.2018.03.003
[24]	Xu C, Liu M S, Zhang C, et al. The spatiotemporal dynamics of rapid urban growth in the Nanjing metropolitan region of China. Landscape Ecology, 2007, 22(6):925-937. doi: 10.1007/s10980-007-9079-5
[25]	Jiao L M, Mao L F, Liu Y L. Multi-order Landscape Expansion Index: Characterizing urban expansion dynamics. Landscape and Urban Planning, 2015, 137:30-39. doi: 10.1016/j.landurbplan.2014.10.023
[26]	Yue Wenze, Wang Ruiliang, Fan Beilei. Spatial patterns analysis of urban expansion in Hangzhou city. Journal of Zhejiang University (Science Edition), 2013, 40(5):596-605.
	[ 岳文泽, 汪锐良, 范蓓蕾. 城市扩张的空间模式研究: 以杭州市为例. 浙江大学学报(理学版), 2013, 40(5):596-605.]
[27]	Liu X P, Li X, Chen Y M, et al. A new landscape index for quantifying urban expansion using multi-temporal remotely sensed data. Landscape Ecology, 2010, 25(5):671-682. doi: 10.1007/s10980-010-9454-5
[28]	Lai L, Huang X J, Yang H, et al. Carbon emissions from land-use change and management in China between 1990 and 2010. Science Advances, 2016, 2(11):e1601063. DOI: 10.1126/sciadv.1601063. doi: 10.1126/sciadv.1601063
[29]	Tan R H, Liu Y L, Zhou K H, et al. A game-theory based agent-cellular model for use in urban growth simulation: A case study of the rapidly urbanizing Wuhan area of central China. Computers,Environment and Urban Systems, 2015, 49:15-29. doi: 10.1016/j.compenvurbsys.2014.09.001
[30]	Duncan B, Sabagh G, Van Arsdol M D. Patterns of city growth. American Journal of Sociology, 1962, 67(4):418-429. doi: 10.1086/223165
[31]	Aguilera F, Valenzuela L M, Botequilha-Leitão A. Landscape metrics in the analysis of urban land use patterns: A case study in a Spanish metropolitan area. Landscape and Urban Planning, 2011, 99(3-4):226-238. doi: 10.1016/j.landurbplan.2010.10.004

种子点数量	1	2	3	4	5	6	7	8
整体部分精度	0.8632	0.8648	0.8724	0.8737	0.8748	0.8753	0.8721	0.8719
新增部分精度	0.4818	0.4866	0.5134	0.5136	0.5207	0.5217	0.5132	0.5015

区名	边缘型占比	填充型占比	飞地型占比
洪山区	67.19	32.81	0.00
江夏区	63.44	36.56	0.00
蔡甸区	63.77	5.74	30.49
汉阳区	80.53	19.47	0.00
江岸区	3.42	96.58	0.00
东西湖区	71.09	28.91	0.00
黄陂区	40.12	41.84	18.04
硚口区	98.99	1.01	0.00
汉南区	47.36	52.64	0.00
新洲区	10.04	89.96	0.00
武昌区	55.43	44.57	0.00
青山区	30.22	69.78	0.00
江汉区	0.00	100.00	0.00

Synchronized simulation of urban diffusional and aggregational process based on the affinity propagation cellular automata: A case study of Wuhan city

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 31

Related Articles 15

Metrics

Comments

[1]	WU Hao, JIANG Zhimeng, LIN Anqi, ZHU Wenchao, WANG Wei. Analyzing spatial characteristics of urban resource and environment carrying capacity based on Covert-Resilient-Overt:A case study of Wuhan city [J]. Acta Geographica Sinica, 2021, 76(10): 2439-2458.
[2]	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.
[3]	Limin JIAO, Zehui LI, Gang XU, Boen ZHANG, Ting DONG, Yanyan GU. The characteristics and patterns of spatially aggregated elements in urban areas of Wuhan [J]. Acta Geographica Sinica, 2017, 72(8): 1432-1443.
[4]	ZHENG Wensheng,ZHUO Rongrong,LUO Jing,YU Bin,WANG Xiaofang. The distribution environment of robbery, snatch and theft crime based on space syntax: A case study of the central area of Wuhan [J]. Acta Geographica Sinica, 2016, 71(10): 1710-1720.
[5]	Jun YANG, Peng XIE, Jianchao XI, Quansheng GE, Xueming LI, Zhandong MA. LUCC simulation based on the cellular automata simulation: A case study of Dalian Economic and Technological Development Zone [J]. Acta Geographica Sinica, 2015, 70(3): 461-475.
[6]	ZHANG Zuo, LI Jiangfeng, CHEN Shuang, LIU Yanzhong. Spatial Distribution of Affordable Houses in Cities: A Case Study of Wuhan Based on DEM [J]. Acta Geographica Sinica, 2011, 66(10): 1309-1320.
[7]	LONG Ying, SHEN Zhen-jiang, MAO Qi-zhi, DANG An-rong. Form Scenario Analysis Using Constrained Cellular Automata [J]. Acta Geographica Sinica, 2010, 65(6): 643-655.
[8]	FENG Yong-jiu, LIU Miao-long, TONG Xiao-hua, LIU Yan, HAN Zhen. Kernel Principal Components Analysis Based Cellular Model for Restructuring and Predicting Urban Evolution [J]. Acta Geographica Sinica, 2010, 65(6): 665-675.
[9]	LONG Ying, HAN Hao-Yang, MAO Ji-Zhi. Establishing Urban Growth Boundaries Using Constrained CA [J]. Acta Geographica Sinica, 2009, 64(8): 999-1008.
[10]	LI Jia, LIU Xiao-Beng, HE Jin-Jiang, LI Dan, CHEN Yi-Min, LONG Yao, LI Shao-Yang. A Geographical Simulation and Optimization System Based on Coupling Strategies [J]. Acta Geographica Sinica, 2009, 64(8): 1009-1018.
[11]	LIU Cheng-Liang-1, 2, Tu-Rui-Lin-3, Xiong-Jian-Beng-2, Ceng-Ju-Xin-1. Spatial Accessibility of Road Network in Wuhan Metropolitan Area [J]. Acta Geographica Sinica, 2009, 64(12): 1488-1498.
[12]	LI Xia, LIU Xiaoping. Case-based Cellular Automaton for Simulating Urban Development in a Large Complex Region [J]. Acta Geographica Sinica, 2007, 62(10): 1097-1109.
[13]	YANG Qingsheng, LI Xia. Mining Transition Rules for Geo-simulation Using Rough Sets [J]. Acta Geographica Sinica, 2006, 61(8): 882-894.
[14]	LIU Xiaoping, LI Xia. Retrieving CA Nonlinear Transition Rule from High-dimensional Feature Space [J]. Acta Geographica Sinica, 2006, 61(6): 663-672.
[15]	LIU Xiaoping, LI Xia, AI Bin, TAO Haiyan, WU Shaokun, LIU Tao. Multi-agent Systems for Simulating and Planning Land Use Development [J]. Acta Geographica Sinica, 2006, 61(10): 1101-1112.