Study on Mountain City Landscape Gradient Characteristics and Urban Construction Coupling: Taking the Yangtze River to the Eastern Ridge Line of Nan Mountain in Chongqing as an Example

Since the reform and opening up, China’s economy has maintained rapid growth. At the same time, the process of urbanization in China has been accelerating and the scale of urban construction land has expanded accordingly. The purpose of the research is to explore whether there is an inevitable connection between the expansion of urban construction land and economic growth. This study uses 108 prefecture-level cities in the Yangtze River Economic Belt as an example. Considering panel data from 2005 to 2015, the spatial econometric model was used to explore the impact of urban construction land expansion on regional economic growth. The results are as follows: (1) The expansion of construction land in cities in the Yangtze River Economic Belt has a significant impact on economic growth but the extent of the impact is not as great as that of capital stock. (2) In the Yangtze River Economic Belt, the expansion of urban construction land in a certain area has not only a positive effect on the local economic growth but also a certain spillover effect and it can promote the economic development level of the adjacent areas in the economic belt. (3) Although the expansion of urban construction land along the Yangtze River Economic Belt promotes economic growth, there are obvious differences between regions. The expansion of urban construction land in the central region of the Yangtze River Economic Belt has a significant driving effect on economic growth. However, the expansion of urban construction land in the eastern and western regions has no significant effect on the economic growth of the respective regions. Finally, based on the above conclusions, this paper proposes corresponding policy recommendations for economic development in different regions. These research conclusions will also facilitate the follow-up of other researchers to further explore the driving factors of the economic development of many prefecture-level cities in the Yangtze River Economic Belt and the related mechanisms for the expansion of construction land to promote economic growth.

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The Analysis of the Urban Sprawl Measurement System of the Yangtze River Economic Belt, Based on Deep Learning and Neural Network Algorithm

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17124194 ◽

2020 ◽

Vol 17 (12) ◽

pp. 4194 ◽

Cited By ~ 1

Author(s):

Huafang Huang ◽

Xiaomao Wu ◽

Xianfu Cheng

Keyword(s):

Neural Network ◽

Urban Sprawl ◽

Measurement System ◽

Yangtze River ◽

Evaluation Model ◽

Smart Growth ◽

The Yangtze River ◽

Construction Land ◽

Urban Construction ◽

Neural Network Algorithm

In the context of rapid urbanization, the spread of cities in the Yangtze River Economic Belt is intensifying, which has an impact on the green and sustainable development of these cities. It is necessary to establish an accurate urban sprawl measurement system. First, the regulation theory of urban sprawl is explained. According to the actual development situation of cities in the Yangtze River Economic Belt, smart growth theory is selected as the basic regulation method of urban sprawl. Second, the back propagation neural network (BPNN) algorithm under deep supervised learning is applied to construct a smart evaluation model of land use growth. Finally, based on the actual development of cities in the Yangtze River Economic Belt, the quantitative growth measurement method is selected to construct a measurement system of urban sprawl in the Yangtze River Economic Belt, and the empirical analysis is carried out. The training results show that the proposed BPNN smart growth evaluation model, based on deep supervised learning, has good evaluation accuracy, and the error is within the preset range. The analysis of the quantitative growth-based measurement system in the increase of urban construction land shows that the increase in urban construction land area of the Yangtze River Economic Belt from 2014 to 2019 was 78.67 km2. Meanwhile, the increases in urban construction land area in different years are different. The empirical results show that the population composition of the Yangtze River Economic Belt and the urban construction area between 2005 and 2019 show a trend of increasing annually; at the same time, urban sprawl development shows a staged characteristic. It is of great significance to apply deep learning fusion neural network algorithm in the construction of the urban sprawl measurement system, which provides a quantitative basis for the in-depth analysis and discussion of urban sprawl.

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