基于“多规合一”的市级国土空间优化方法——以烟台市为例

doi:10.18306/dlkxjz.2018.08.004

摘要/Abstract

摘要：

针对现阶段各规划边界交叉、空间重叠的问题,本文从提高空间价值、减少空间破碎度和协调各类空间的角度出发,在梳理现有空间优化模型及智能算法的基础上,改进多目标规划模型并适应性改造遗传算法,以此二者构建市级国土空间优化模型。以烟台市为例,设置3种情景为决策者提供方案集进行3类规划主导下的2020年国土空间优化研究,结果显示：①优化后烟台市农业、城镇和生态空间价值分别增加了23.24%、29.27%、6.30%;②不同情景下空间分布合理且较为集中,生态保护情景有利于国土空间集合连片,经济发展情景更适合于多空间协调发展。研究表明：该模型能有效地解决国土空间内容重叠问题,明显提高了国土空间价值,同时优化模型适应性强,为“多规合一”背景下市级国土空间优化提供技术支撑。

关键词: 多规合一, 国土空间优化, 多目标规划模型, 遗传算法, 烟台市

Abstract:

With the rapid economic growth and the advancement of industrialization and urbanization, China has entered into a new stage of transformation and development since the economic reform and opening-up. However, due to the sectoral management by various planning departments, different planning system and technical standards present a phenomenon of boundary crossing and spatial overlap in the land space. How to solve the problem of boundary adjustment in conflict area caused by different planning and establish a scientific and orderly spatial planning system have become important and difficult issues. Because a large amount of complex spatial information is involved in spatial planning, we also need to consider the natural and socioeconomic attributes of land space. Therefore, spatial planning under such circumstances becomes a multi-objective optimization problem for nonlinear combinations. Traditional mathematical models such as linear programming and grey linear programming cannot meet this requirement. But the development of geographic information technology provides important support for spatial optimization. Scholars have combined mathematical models with the algorithm to explore the quantitative structure and spatial optimization of regional land space. Yet, these studies essentially take land use into consideration, ignoring the systematic and hierarchical nature of land space. In addition, they have mainly considered land use planning or master urban planning but not considered the optimization of various land spatial conflicts under different planning schemes. On the basis of existing spatial optimization models and intelligent algorithm, a model of land spatial optimization with both multi-objective programming and genetic algorithm has been constructed in this study. This model aims to improve the spatial value, reduce the degree of spatial fragmentation, and coordinate various kinds of space. In order to simulate different optimization results and provide solutions for decision makers, three scenarios were set up in this study. Taking Yantai City as the research area, the multi-objective programming model was chosen to carry out the study of land spatial optimization under three kinds of planning in 2020. By using the optimization model, land space efficiency has been significantly improved, with agricultural, urban, and ecological space values increased by 23.24%, 29.27%, and 6.30%. Agricultural, urban, and ecological space values have reached 1.17×10⁹, 1.14×10¹⁰, and 6.44×10⁷ yuan. After optimization, all kinds of spaces are reasonably distributed and spatial aggregation is increased. Spatial aggregation has reached the highest level with the value of 1.5876 in the ecological protection scenario, and the degree of spatial coordination reaches 2.5245 in the economic development scenario, which is the highest of all scenarios. The experimental results show that the optimization model in this study can effectively solve the problem of land space overlap and promote effective allocation of land space. Moreover, it can improve agricultural, urban, and ecological values in the land space and coordinate the development goals of different planning. It provides a technical support for land spatial optimization in the context of "multiple planning integration" in the future.

Key words: multiple planning integration, land spatial optimization, multi-objective programming model, genetic algorithm, Yantai City

张骏杰, 高延利, 蔡玉梅, 周伟, 袁涛. 基于“多规合一”的市级国土空间优化方法——以烟台市为例[J]. 地理科学进展, 2018, 37(8): 1045-1054.

Junjie ZHANG, Yanli GAO, Yumei CAI, Wei ZHOU, Tao YUAN. Spatial optimization on the municipal level based on "multiple planning integration": A case study of Yantai City[J]. PROGRESS IN GEOGRAPHY, 2018, 37(8): 1045-1054.

图/表 12

图1

图2

图3

图4

表1

图5

表2

表3

表4

表5

图6

表6

参考文献 28

[1]	陈国良, 刘笃慧. 1983. 黄土丘陵区农林牧合理生态经济结构模式的初步研究[J]. 植物生态学与地植物学丛刊, 7(3): 215-221.
	[Chen G L, Liu D H.1983. A preliminary study on the rational eco-economic structure model of agriculture, forestry and animal husbandry in the Loess Hilly Regions[J]. Acta Phytoecologica et Geobotanica Sinica, 7(3): 215-221.]
[2]	樊杰. 2016. 我国国土空间开发保护格局优化配置理论创新与“十三五”规划的应对策略[J]. 中国科学院院刊, 31(1): 1-12.
	[Fan J.2016. Theoretical innovation in optimization of protection and development of China's territorial space and coping strategy of 13th Five-Year Plan[J]. Bulletin of Chinese Academy of Sciences, 31(1): 1-12.]
[3]	冯仕超, 高小红, 顾娟, 等. 2013. 基于CLUE-S模型的湟水流域土地利用空间分布模拟[J]. 生态学报, 33(3): 985-997. doi: 10.5846/stxb201204280616
	[Feng S C, Gao X H, Gu J, et al.2013. Land use spatial distribution modeling based on CLUE-S model in the Huangshui River Basin[J]. Acta Ecologica Sinica, 33(3): 985-997.] doi: 10.5846/stxb201204280616
[4]	郭小燕, 刘学录, 王联国. 2015. 基于混合蛙跳算法的土地利用格局优化[J]. 农业工程学报, 31(24): 281-288.
	[Guo X Y, Liu X L, Wang L G.2015. Land use pattern optimization based on shuffled frog leaping algorithm[J]. Transactions of the Chinese Society of Agricultural Engineering, 31(24): 281-288.]
[5]	胡云锋, 王倩倩, 刘越, 等. 2011. 国家尺度社会经济数据格网化原理和方法[J]. 地球信息科学学报, 13(5): 573-578. doi: 10.3724/SP.J.1047.2011.00573
	[Hu Y F, Wang Q Q, Li Y, et al.2011. Index system and transferring methods to build the national society and economy grid database[J]. Journal of Geo-Information Science, 13(5): 573-578.] doi: 10.3724/SP.J.1047.2011.00573
[6]	黄迎春, 杨伯钢, 张飞舟, 等. 2016. 基于同类城市建设目标的北京市土地利用结构优化[J]. 农业工程学报, 32(4): 217-227. doi: 10.11975/j.issn.1002-6819.2016.04.031
	[Huang Y C, Yang B G, Zhang F Z, et al.2016. Optimization of land use structure in Beijing based on goal of similar city in world[J]. Transactions of the Chinese Society of Agricultural Engineering, 32(4): 217-227.] doi: 10.11975/j.issn.1002-6819.2016.04.031
[7]	黄勇, 周世锋, 王琳, 等. 2016. “多规合一”的基本理念与技术方法探索[J]. 规划师, 32(3): 82-88. doi: 10.3969/j.issn.1006-0022.2016.03.013
	[Huang Y, Zhou S F, Wang L, et al.2016. Basic theory and technique of multi-plans integration[J]. Planners, 32(3): 82-88.] doi: 10.3969/j.issn.1006-0022.2016.03.013
[8]	李飞, 张树文, 杨久春, 等. 2014. 社会经济数据空间化研究进展[J]. 地理与地理信息科学, 30(4): 102-107. doi: 10.3969/j.issn.1672-0504.2014.04.021
	[Li F, Zhang S W, Yang J C, et al.2014. A review on research about spatialization of socioeconomic data[J]. Geography and Geo-Information Science, 30(4): 102-107.] doi: 10.3969/j.issn.1672-0504.2014.04.021
[9]	林坚, 柳巧云, 李婧怡. 2016. 探索建立面向新型城镇化的国土空间分类体系[J]. 城市发展研究, 23(4): 51-60.
	[Lin J, Liu Q Y, Li J Y.2016. Research on establishing a land space utilization classification for new urbanization[J]. Urban Development Studies, 23(4): 51-60.]
[10]	刘彦随, 王介勇. 2016. 转型发展期“多规合一”理论认知与技术方法[J]. 地理科学进展, 35(5): 529-536. doi: 10.18306/dlkxjz.2016.05.001
	[Liu Y S, Wang J Y.2016. Theoretical analysis and technical methods of "multiple planning integration" in the rural to urban transition period in China[J]. Progress in Geography, 35(5): 529-536.] doi: 10.18306/dlkxjz.2016.05.001
[11]	刘耀林, 洪晓峰, 刘殿锋, 等. 2011. 利用模拟退火算法的村镇土地利用空间优化调控模型[J]. 武汉大学学报: 信息科学版, 36(6): 752-755.
	[Liu Y L, Hong X F, Liu D F, et al.2011. Optimal spatial regulation model for village and town land-use based on simulated annealing[J]. Geomatics and Information Science of Wuhan University, 36(6): 752-755.]
[12]	罗鼎, 许月卿, 邵晓梅, 等. 2009. 土地利用空间优化配置研究进展与展望[J]. 地理科学进展, 28(5): 791-797.
	[Luo D, Xu Y Q, Shao X M, et al.2009. Advances and prospects of spatial optimal allocation of land use[J]. Progress in Geography, 28(5): 791-797.]
[13]	马世发, 何建华, 俞艳. 2010. 基于粒子群算法的城镇土地利用空间优化模型[J]. 农业工程学报, 26(9): 321-326. doi: 10.3969/j.issn.1002-6819.2010.09.053
	[Ma S F, He J H, Yu Y.2010. Model of urban land-use spatial optimization based on particle swarm optimization algorithm[J]. Transactions of the Chinese Society of Agricultural Engineering, 26(9): 321-326.] doi: 10.3969/j.issn.1002-6819.2010.09.053
[14]	王红瑞, 张文新, 胡秀丽, 等. 2008. 土地利用区间数多目标规划模型及其应用[J]. 农业工程学报, 24(8): 68-73.
	[Wang H R, Zhang W X, Hu X L, et al.2008. Model of interval multi-objective programming of land-use and its application[J]. Transactions of the Chinese Society of Agricultural Engineering, 24(8): 68-73.]
[15]	肖金成, 欧阳慧. 2012. 优化国土空间开发格局研究[J]. 经济学动态, (5): 18-23.
	[Xiao J C, Ouyang H.2012. Youhua guotu kongjian kaifa geju yanjiu[J]. Economic Perspectives, (5): 18-23.]
[16]	谢高地, 肖玉, 甄霖, 等. 2005. 我国粮食生产的生态服务价值研究[J]. 中国生态农业学报, 13(3): 10-13.
	[Xie G D, Xiao Y, Zhen L, et al.2005. Study on ecosystem services value of food production in China[J]. Chinese Journal of Eco-Agriculture, 13(3): 10-13.]
[17]	谢高地, 张彩霞, 张雷明, 等. 2015. 基于单位面积价值当量因子的生态系统服务价值化方法改进[J]. 自然资源学报, 30(8): 1243-1254.
	[Xie G D, Zhang C X, Zhang L M, et al.2015. Improvement of the evaluation method for ecosystem service value based on per unit area[J]. Journal of Natural Resources, 30(8): 1243-1254.]
[18]	谢正峰, 董玉祥. 2011. 我国城市土地优化配置研究演进与展望[J]. 经济地理, 31(8): 1364-1369.
	[Xie Z F, Dong Y X.2011. Evolution and prospect of China's research on the urban land optimal allocation[J]. Economic Geography, 31(8): 1364-1369.]
[19]	姚作芳, 刘兴土, 杨飞. 2010. 马尔科夫方法修正的灰色模型在吉林省粮食产量预测中的应用[J]. 地理科学, 30(3): 452-457.
	[Yao Z F, Liu X T, Yang F.2010. Application of grey model modified by Markov method in the yield prediction of Jilin Province[J]. Scientia Geographica Sinica, 30(3): 452-457.]
[20]	喻锋, 张丽君, 李晓波, 等. 2014. 国土空间开发及格局优化研究: 现状述评、战略方向、技术路径与总体框架[J]. 国土资源情报, (8): 41-46.
	[Yu F, Zhang L J, Li X B, et al.2014. Research on spatio-land development and its pattern optimization: Status-quo review, strategic orientations, technology paths and overall framework[J]. Land and Resources Information, (8): 41-46.]
[21]	喻胜华, 龚尚花. 2016. 基于Lasso和支持向量机的粮食价格预测[J]. 湖南大学学报: 社会科学版, 30(1): 71-75.
	[Yu S H, Gong S H.2016. A study on grain price prediction based on Lasso and support vector machine[J]. Journal of Hunan University: Social Sciences, 30(1): 71-75.]
[22]	袁满, 刘耀林. 2014. 基于多智能体遗传算法的土地利用优化配置[J]. 农业工程学报, 30(1): 191-199. doi: 10.3969/j.issn.1002-6819.2014.01.025
	[Yuan M, Liu Y L.2014. Land use optimization allocation based on multi-agent genetic algorithm[J]. Transactions of the Chinese Society of Agricultural Engineering, 30(1): 191-199.] doi: 10.3969/j.issn.1002-6819.2014.01.025
[23]	张丁轩, 付梅臣, 陶金, 等. 2013. 基于CLUE-S模型的矿业城市土地利用变化情景模拟[J]. 农业工程学报, 29(12): 246-256. doi: 10.3969/j.issn.1002-6819.2013.12.031
	[Zhang D X, Fu M C, Tao J, et al.2013. Scenario simulation of land use change in mining city based on CLUE-S model[J]. Transactions of the Chinese Society of Agricultural Engineering, 29(12): 246-256.] doi: 10.3969/j.issn.1002-6819.2013.12.031
[24]	张鸿辉, 曾永年, 刘慧敏. 2011. 多目标土地利用空间优化配置模型及其应用[J]. 中南大学学报: 自然科学版, 42(4): 1056-1065.
	[Zhang H H, Zeng Y N, Liu H M.2011. Multi-objective spatial optimization model for land use allocation and its application[J]. Journal of Central South University: Science and Technology, 42(4): 1056-1065.]
[25]	Cao K, Huang B, Wang S, et al.2012. Sustainable land use optimization using Boundary-based Fast Genetic Algorithm[J]. Computers Environment and Urban Systems, 36(3): 257-269. doi: 10.1016/j.compenvurbsys.2011.08.001
[26]	Gharbia S S, Alfatah S A, Gill L, et al.2016. Land use scenarios and projections simulation using an integrated GIS cellular automata algorithms[J]. Modeling Earth Systems and Environment, 2(3): 151.
[27]	Kent B, Bare B B, Field R C, et al.1991. Natural resource land management planning using large-scale linear programs: The USDA Forest Service experience with FORPLAN[J]. Operations Research, 39(1): 13-27.
[28]	Stewart T J, Janssen R.2014. A multiobjective GIS-based land use planning algorithm[J]. Computers Environment and Urban Systems, 46(7): 25-34. doi: 10.1016/j.compenvurbsys.2014.04.002

国土空间	主要内容	与三规对照
农业空间	以提供农产品服务为主,满足人类基本生存的空间。根据土地利用总体规划、基本农田分布图及农用地分等定级图划分为基本农田及一般耕地。其中包括零星的农村建设用地等。	土地利用总体规划的基本农田保护区、一般农地区及自然保留地等
城镇空间	以提供工业品或服务产品为主,促进人类发展进步的空间。以城镇建设用地为主,根据各县城市总体规划划分为建成区及拓展区域。	城市总体规划的居住用地、商业服务业设施用地及工业用地等
生态空间	以提供生态系统服务为主,保障社会可持续发展的空间。根据生态保护红线规划重新划分为禁止开发区及二级管控区。包括农用地中的非耕地等。	生态保护红线规划的结果,包括生态保护红线区和Ι类红线区

变量	内容
农业空间	与其他空间冲突的一般耕地
城镇空间	与其他空间冲突的拓展区域
生态空间	与其他空间冲突的二级管控区

国土空间	粮食安全情景			经济发展情景			生态保护情景
国土空间	AS	US	ES	AS	US	ES	AS	US	ES
AS	1.0	0.6	0.8	1.0	0.8	0.6	1.0	0.4	0.8
US	0.6	1.0	0.4	0.8	1.0	0.4	0.4	1.0	0.4
ES	0.8	0.4	1.0	0.6	0.4	1.0	0.8	0.4	1.0

情景	单位空间价值/元			单位紧凑度	单位协调度
情景	农业空间	城镇空间	生态空间	单位紧凑度	单位协调度
粮食安全	53981	511913	2981	1.5508	2.3744
经济发展	53235	533450	2799	1.5629	2.5245
生态保护	52902	506920	3008	1.5876	2.2228