基于MCR与Markov-CA模型的城镇增长边界划定研究——以江苏省新沂市为例
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摘要
从城镇用地外部条件对城镇扩张的影响和城镇用地内在发展规律两个方面出发,通过耦合最小累积阻力模型与Markov-CA模型,探讨了一种结合阻力分析与动力分析的城镇增长边界划定思路,并以新沂市为例进行实证分析。首先,结合研究区实际情况,选取相对阻力因子,通过MCR模型对城镇扩张建立最小累积阻力图, 从而获得城镇增长刚性边界;其次,以Markov-CA模型作为动力模拟模型预测2020年的城镇扩张,从而划定城镇增长弹性边界;最后,综合城镇刚性增长边界、2020年城镇用地扩张模拟结果,获得2020年最终的城镇增长边界。结果表明,研究区城镇增长边界内总面积为154.15km~2,占研究区国土总面积的9.7%。
Based on the influence of the external conditions of urban land use on urban expansion and the intrinsic developing rules of urban land development, through the combination of urban minimum growth resistance model and Markov-CA model, this article discusses an idea of the urban growth boundary delimitation from combined resistance analysis and dynamic analysis by taking Xinyi City as an example for empirical analysis. Firstly, based on the actual situation of the study area, the relative resistance factor is selected, and the minimum cumulative resistance map is established for urban expansion through the MCR model to obtain the rigid boundary of urban growth. Secondly, the Markov-CA model is used as the dynamic simulation model to predict the urban expansion in 2020. In this way, the elastic growth boundary of urban growth is delineated. Finally, the urban rigid growth boundary and the urban land expansion simulation result in 2020 will be used to obtain the final urban growth boundary in 2020. The results show that the total area within the urban growth boundary of the study area is 154.15 km2, accounting for 9.7% of the total land area in the study area.
Based on the influence of the external conditions of urban land use on urban expansion and the intrinsic developing rules of urban land development, through the combination of urban minimum growth resistance model and Markov-CA model, this article discusses an idea of the urban growth boundary delimitation from combined resistance analysis and dynamic analysis by taking Xinyi City as an example for empirical analysis. Firstly, based on the actual situation of the study area, the relative resistance factor is selected, and the minimum cumulative resistance map is established for urban expansion through the MCR model to obtain the rigid boundary of urban growth. Secondly, the Markov-CA model is used as the dynamic simulation model to predict the urban expansion in 2020. In this way, the elastic growth boundary of urban growth is delineated. Finally, the urban rigid growth boundary and the urban land expansion simulation result in 2020 will be used to obtain the final urban growth boundary in 2020. The results show that the total area within the urban growth boundary of the study area is 154.15 km2, accounting for 9.7% of the total land area in the study area.
引文
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[25]舒帮荣,刘友兆,张鸿辉,等.集成变权与约束性模糊CA的城镇用地扩张情景模拟[J].武汉大学学报(信息科学版),2013,38(4):498-504.
[26]黄丽明,陈健飞.广州市花都区城镇建设用地适宜性评价研究:基于MCR面特征提取[J].资源科学,2014,36(7):1347-1355.
[27]叶玉瑶,苏泳娴,张虹鸥,等.生态阻力面模型构建及其在城市扩展模拟中的应用[J].地理学报,2014,69(4):485-496.
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[2]Hualou Long,Yongqiang Liu,Xuegang Hou,et al.Effects of land use transitions due to rapid urbanization on ecosystem services:implications for urban planning in the new developing area of china[J].Habitat Int,2014(44):536-544.
[3]邱思齐,岳文泽.刚性与弹性相结合的城市开发边界划定:以浙江省义乌市为例[J].应用生态学报,2018,29(5):1607-1616.
[4]Xun Liang,Xiaoping Liu,Xia Li,et al.Delineating multi-scenario urban growth boundaries with a ca-based flus model and morphological method[J].Landsc Urban Plan,2018(177):47-63.
[5]何京.从“集中建设区”走向“城市开发边界”:试论上海的土地规划空间管制[J].上海城市规划,2015(5):81-86,109.
[6]鲁智杞,甘淑,张述清,等.“多规合一”背景下城镇开发边界划定实践研究[J].昆明理工大学学报(自然科学版),2018,43(3):119-124.
[7]朱寿红,舒帮荣,马晓冬,等.基于“反规划”理念及FLUS模型的城镇用地增长边界划定研究:以徐州市贾汪区为例[J].地理与地理信息科学,2017,33(5):80-86,127.
[8]丛佃敏,赵书河,于涛,等.综合生态安全格局构建与城市扩张模拟的城市增长边界划定:以天水市规划区(2015-2030年)为例[J].自然资源学报,2018,33(1):14-26.
[9]李旭锋.哈尔滨城市空间增长边界设定研究[D].哈尔滨:哈尔滨工业大学,2010.
[10]祝仲文,莫滨,谢芙蓉.基于土地生态适宜性评价的城市空间增长边界划定:以防城港市为例[J].规划师,2009,25(11):40-44.
[11]陈伟强,潘元庆,马月红,等.基于约束性CA模型的城市开发边界划定方法[J].农业工程学报,2017,33(4):278-284.
[12]徐海龙,尹海伟,孔繁花,等.基于潜力-约束和SLEUTH模型松散耦合的南京城市扩展模拟[J].地理研究,2017,36(3):529-540.
[13]蒋海波,石培基,李骞国,等.基于SLEUTH模型的酒嘉一体化城市扩展预测[J].干旱区资源与环境,2017,31(1):25-31.
[14]Shifa Ma,Xia Li,Yumei Cai.Delimiting the urban growth boundaries with a modified ant colony optimization model[J].Comput Environ Urban Syst,2016.
[15]DONG Guanglong,XU Erqi,ZHANG Hongqi.Urban expansion and spatiotemporal relationships with driving factors revealed by geographically weighted logistic regression[J].J Resour Ecol,2017,8(3):277-286.
[16]张洁.基于CA-SVM模型的福建省莆田市城市空间动态扩张研究[D].武汉:中国地质大学(武汉),2017.
[17]付玲,胡业翠,郑新奇.基于BP神经网络的城市增长边界预测:以北京市为例[J].中国土地科学,2016,30(2):22-30.
[18]龙瀛,韩昊英,毛其智.利用约束性CA制定城市增长边界[J].地理学报,2009,64(8):999-1008.
[19]唐丽,罗亦殷,罗改改,等.基于粒度反推法和MCR模型的海南省东方市景观格局优化[J].生态学杂志,2016,35(12):3393-3403.
[20]黄丽明,陈健飞.广州市花都区城镇建设用地适宜性评价研究:基于MCR面特征提取[J].资源科学,2014,36(7):1347-1355.
[21]孟成,卢新海,彭明军,等.基于Markov-C 5.0的CA城市用地布局模拟预测方法[J].中国土地科学,2015,29(6):82-88,97.
[22]经阳.基于CA-Markov模型的佛山市生态用地演变及其模拟[D].抚州:东华理工大学,2016.
[23]勒明凤.基于CA-Markov模型的香格里拉县城市增长边界设定研究[D].昆明:云南大学,2014.
[24]COURAGE kamusoko,Jonah Gamba.Simulating urban growth using a random forest-cellular automata(rf-ca)model[J].IJGI,2015,4(2):447-470.
[25]舒帮荣,刘友兆,张鸿辉,等.集成变权与约束性模糊CA的城镇用地扩张情景模拟[J].武汉大学学报(信息科学版),2013,38(4):498-504.
[26]黄丽明,陈健飞.广州市花都区城镇建设用地适宜性评价研究:基于MCR面特征提取[J].资源科学,2014,36(7):1347-1355.
[27]叶玉瑶,苏泳娴,张虹鸥,等.生态阻力面模型构建及其在城市扩展模拟中的应用[J].地理学报,2014,69(4):485-496.
[28]郝庆,封志明,袁国华.省级国土空间规划编制的几点思考[J].中国国土资源经济,2018,31(1):29-33.