PROGRESS IN GEOGRAPHY ›› 2018, Vol. 37 ›› Issue (8): 1045-1054.doi: 10.18306/dlkxjz.2018.08.004

• Articles • Previous Articles     Next Articles

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;
  • Supported by:
    International Scientific and Technological Cooperation Program of China, No.2015DFA01370


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