耦合管控效应的城市空间多情景模拟与政策分析

doi:10.13959/j.issn.1003-2398.2017.03.009

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Abstract The temporal and spatial evolution of urban expansion is a complicated dynamic system, and it is very important to master law of this evolution in land management and urban development planning. Traditional simulation of land use change focused more on the development law of land use space, and but less on the analysis of land regulation mechanism.The evaluation of land policy was mostly carried out by selecting characteristic index to analyze the situation prior and subsequent to policy implementation, with the demerits of time lag and small application scope. The combination of urban cellular automata and land policy evaluation is to estimate and predict the effectiveness of land use change, and to identify the risk of ecological damage. This paper attempts to simulate multi-scenario and conduct an analysis of the policy of urban space under the effects of coupling and control by embedding into the transitional rules of cellular automata the following schemes, namely quantitative management of urban expansion, differential handling of land, and zoning treatment of land use. Then logistic regression method to mining city spatial development inertia is employed and a model basing on multi policy compound control is constructed to simulate the planning period of future urban construction of Jiangxia. Finally, the result is compared with the prediction of each single control.

Key words： land policy aontrolling effect multi-scenario simulation cellular automata (CA) urban expansion

Received: 20 November 2015

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http://rwdl.xisu.edu.cn/EN/10.13959/j.issn.1003-2398.2017.03.009 OR http://rwdl.xisu.edu.cn/EN/Y2017/V32/I3/68

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[41]	Geertman S, Hagoort M, Ottens H. Spatial-temporal specific neighborhood rules for cellular automata land-use modeling[J]. International Journal of Geographical Information Science, 2007,21(5): 547-568.
[45]	He C, Okada N, Zhang Q, et al. Modelling dynamic urban expansion processes incorporating a potential model with cellular automata[J]. Landscape and Urban Planning, 2008,86(1):79-91.
[42]	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[J].地球信息科学学报,2013,33(10):1246-1250. [Ma Shifa, Ai Bin, Ou Jinpei. Spatial allocation of construction land with constrained cellular automata model[J]. Scientia Geographica Sinica, 2013,33(10):1246-1250.]
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[47]	Jokar Arsanjani J, Helbich M, Kainz W, et al. Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion[J]. International Journal of Applied Earth Observation and Geo-information, 2013,21(1):265-275.
[44]	黎夏,叶嘉安.基于神经网络的单元自动机CA及真实和优化的城市模拟[J]. 地理学报,2002,57(2):159-166. [Li Xia, Yeh Anthony Gar-On. Neural-network-based cellular automata for realistic and idealized urban simulation[J]. Acta Geographica Sinica, 2002,57(2): 159-166.]
[48]	马世发,艾彬,念沛豪.基于约束性CA的土地利用规划预评估及警情探测[J].地理与地理科学信息,2014,30(4):52-54. [Ma Shifa, Ai Bin, Nian Peihao. Pre-assessment and warning of land use planning with constrained cellular automata[J]. Geography and Geo-Information Science, 2014,30(4):52-54.]
[45]	He C, Okada N, Zhang Q, et al. Modelling dynamic urban expansion processes incorporating a potential model with cellular automata[J]. Landscape and Urban Planning, 2008,86(1):79-91.
[49]	何春阳,陈晋,史培军,等.大都市区城市扩展模型——以北京城市扩展模拟为例[J].地理学报,2003,58(2):294-304. [He Chunyang, Chen Jin, Shi Peijun, et al. City expansion model of metropolitan area in China: A case study of Beijing[J]. Acta Geographica Sinica, 2003,58(2):294-304.]
[46]	Yang J, Wang Z, Yang D, et al. Ecological risk assessment of genetically modified crops based on cellular automata modeling[J]. Biotechnology Advances, 2009,27(6):1132-1136.
[50]	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[J].地理学报,2010,65(6):644-655. [Long Ying, Shen Zhenjiang, Mao Qizhi, et al. Form scenario analysis using constrained cellular automata[J]. Acta Geographica Sinica, 2010,65(6):644-655.]
[51]	邓羽. 城市空间扩展的自组织特征与规划管控效应评估——以北京市为例[J].地理研究,2016,35(2):353-362. [Deng Yu. Self-organization characteristics of urban extension and the planning effect evaluation: A case study of Beijing[J]. Geographical Research, 2016, 35(2):353-362.]