Spatio-temporal evolution and driving factors of new urbanization in central China based on multi-source data.

Urbanization is an inevitable outcome of the development of human society to a certain stage, and it is also an irreversible pattern of the concentration degree of human society. Based on multi-source data such as remote sensing images, ecological environment and socio-economic data, the evaluation index system of new urbanization is constructed from multi-dimensions of population, economy, society, space and ecology. To explore the spatio temporal evolution and driving factors of urbanization in 80 prefecture-level cities in central China from 2013 to 2021 by using entropy method, spatial autocorrelation model and geographic detector. The results show that: (1) The level of new urbanization continues to grow, with the average value rising from 0.1562 in 2013 to 0.2557 in 2021, and the regional differences are obvious, forming a circle structure with Wuhan, Zhengzhou and other provincial capitals as the center and weakening radiation to surrounding cities. (2) The agglomeration of ecological urbanization is significant, and the agglomeration trend is gradually enhanced. The high-high agglomeration areas tend to Xinzhou City, most prefecture-level cities in Hubei Province and some prefecture-level cities in Southern Hunan Province, while the low-low agglomeration areas tend to Changzhi City, most prefecture level cities in Henan Province and some prefecture-level cities in Northern Anhui Province. (3) The night light index, total retail sales of consumer goods, investment in fixed assets, proportion of built-up areas and urban economic density are the main driving factors affecting the level of new urbanization. (4) The interaction of driving factors shows double factor enhancement and nonlinear enhancement effects.

Study on the Spatial Interaction between Urban Economic and Ecological Environment-A Case Study of Wuhan City.

In order to study the interactive relationship between urban economic and ecological environment, taking Wuhan as an example, Landsat and MODIS remote sensing satellite data and social and economic data were fused with multisource data, and multidimensional indicators were selected to construct the comprehensive evaluation index system of urban economic and ecological environment. The weights were determined by combining subjective and objective methods. Then, the decoupling elasticity coefficient method and spatial autocorrelation model were used to evaluate the dynamic relationship and spatial relationship between economic development and ecological environment in Wuhan from 2014 to 2020. The results showed that there was an interaction between the urban economic and the ecological environment in Wuhan. The ecological level index had a spatial effect, the adjustment of industrial structure had a positive effect on the improvement of the ecological level, and the improvement of the ecological level was also helpful to promote economic development. The typical districts of Huangpi District, Xinzhou District, Jiangxia District, Hannan District, Caidian District, and Hongshan District had superior location and ecological advantages, as well as high development potential. Lastly, on the basis of the empirical analysis results, policy suggestions are made from four aspects: regional differentiated construction, green development, energy consumption, and wetland construction.