地理科学进展 ›› 2018, Vol. 37 ›› Issue (8): 1045-1054.doi: 10.18306/dlkxjz.2018.08.004

• 研究论文 • 上一篇    下一篇

基于“多规合一”的市级国土空间优化方法——以烟台市为例

张骏杰1(), 高延利2, 蔡玉梅2,*(), 周伟1, 袁涛1   

  1. 1. 中国地质大学(北京)土地科学技术学院,北京 100083
    2. 中国土地勘测规划院,北京 100035
  • 收稿日期:2017-08-24 修回日期:2018-03-09 出版日期:2018-09-04 发布日期:2018-09-04
  • 通讯作者: 蔡玉梅 E-mail:zchieh@163.com;caiyumei@263.net
  • 作者简介:

    作者简介:张骏杰(1992-),男,江苏无锡人,硕士研究生,主要从事空间规划相关研究,E-mail: zchieh@163.com

  • 基金资助:
    国家国际科技合作专项(2015DFA01370)

Spatial optimization on the municipal level based on "multiple planning integration": A case study of Yantai City

Junjie ZHANG1(), Yanli GAO2, Yumei CAI2,*(), Wei ZHOU1, Tao YUAN1   

  1. 1. School of Land Science and Technology, China University of Geosciences, Beijing 100083, China
    2. China Land Surveying and Planning Institute, Beijing 100035, China
  • Received:2017-08-24 Revised:2018-03-09 Online:2018-09-04 Published:2018-09-04
  • Contact: Yumei CAI E-mail:zchieh@163.com;caiyumei@263.net
  • Supported by:
    International Scientific and Technological Cooperation Program of China, No.2015DFA01370

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