| In the past 40 years,China’s rapid urbanization has achieved remarkable results.Highly concentrated urban population,facilities,construction and other resources are the direct driving force to promote economic growth,and indirectly promote the transformation of economic development mode and industrial innovation,thus attracting more resources around the city to form a growth cycle.In the process of urbanization,however,there is a lack of scientific and effective research and data support.These problems usually lead to a series of resources,environment and ecology problems,which restrict the sustainable development of the city and reduce the quality of life of the residents.Many scholars have continued to study the above problems,but most of them focus on Beijing,Shanghai,Zhengzhou and other mega-cities,which have entered the late stage of urbanization,which is different from that of Luohe and other mega-cities.But on the other hand,both of them show that the coupling degree of gray space and green space caused by extensive urban development is not good,the power of urban development is not enough,and the traffic infrastructure is gradually showing the guiding function of urban development.In this context,how to solve the problem of LUCC(Land Use/Land Cover Change)under weak driving force to.simulate urban dynamic analysis,grasp the basic characteristics of green space change and the objective law of urban development,predict the future state of land structure under multiple scenarios,study urban growth,and put forward ecological early warning and planning suggestions under multiple scenarios.Under the guidance of the"Complex Giant System Theory" and based on GIS platform,this paper makes analysis and prediction by using fractal index,space syntax,Moran’s I,DEA,grey prediction model and Markov Chain algorithm,and carries out analysis-based simulation by combining the Artificial Neural Network Cellular Automata(ANN-CA)model,so as to carry out quantitative study on the specific characteristics of land use in urban space and time under a complex system,and make macro-description of land use.The main research content and conclusions are as follows.The growth characteristics of gray space of Luohe City from 2004 to 2019 are analyzed from the perspective of morphology.The fractal dimension of construction land has increased from 1.64 in 2004 to 1.70 in 2014,and reached 1.75 in 2019;the coupling degree with green land has increased first and then decreased,reaching the peak of coupling degree around 2014,and the urban form is close to the optimal,and then decreases rapidly with the continuous high-density development of the city.In 2019,the layout of construction land in Luohe City is too dense,and the coupling exchange with green space is blocked.Gray spatial aggregation analysis attempts to select highly aggregated areas from construction land for key transformation:aggregation dimension results show that there are data mutation points,spatial aggregation state is different in different circles,so the region exists;the input-output ratio of ecological land development in the region is higher,which is conducive to guiding smart growth and building a resilient city;meanwhile,the size of the region can also reflect the level and quality of urban development to some extent.In 2004-2019,the accessibility of transportation within the research scope of Luohe City was gradually enhanced,and the accessibility index based on spatial syntax was comprehensively improved.The Moran index-DEA model was used to evaluate the availability of green space resources and the quality of ecological life of residents.The results show that the green space and accessibility spatial layout are not well correlated:the green space with large area and high accessibility concentrates in the periphery of the city and scattered around the Shali River,most of the green space along the river is used poorly and needs to be further improved;in increment,the overall green space investment is insufficient,which indirectly affects the urban development as a short board.Combined with the Markov chain,the gray prediction model predicts the growth of construction land in the research area of Luohe City in 2030,and integrates the natural,population,location,transportation and social economy into five categories and 20 driving factors related to urban development,which are used for the future land use simulation in 2030.Among them,the gray space agglomeration range of ideal scenario falls down,and the fractal value approaches the optimal value.Based on the above index analysis and multi-scenario simulation,the green space of Luohe City is optimized to improve the accessibility of green space and realize the rational allocation of ecological resources.In terms of road network accessibility optimization:based on the results of DEA analysis and Moran Index,the current road network and the green space present no significant correlation in a wide range,the green space areas around the Shali River present low accessibility and high green space concentration,and the accessibility output of each township/street is obviously insufficient.Therefore,traffic construction shall be carried out to raise the overall number of road networks.Taking into account the actual construction situation of the city,it is proposed to improve the output efficiency of road network.The Moran Index shows that priority and emphasis on connecting the road construction with the road network in combination with the five townships/streets with the lowest efficiency value in the DEA result can achieve the best ecological value and the accessibility output of green space.Locally,closed residential areas shall be opened to realize the micro-circulation of traffic.In the aspect of green space form optimization,we should give priority to the optimization and quantity increase of green space form within the maximum agglomeration of construction land to maximize the ecological benefits of green space.According to the quantity and form distribution of the green space in the "ideal scene",the paper puts forward the optimization strategy of the important area and the priority area of the green space increase. |
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