wuhan urban agglomeration
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Land ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 86
Author(s):  
Yanan Li ◽  
Chan Xiong ◽  
Yan Song

China’s urban–rural relationships have been changed dramatically by the intensifying population flows, especially in urban agglomeration regions. This study contributes to the interpretation of urban–rural integration mechanisms in urban agglomeration by constructing a conceptual framework of migration-related resource flows. Taking the Wuhan urban agglomeration as an example, migrants’ farmland arrangement, migration pattern, and social integration have been investigated to uncover the spatial and temporal characteristics of the urban–rural interaction, based on the data from the China Migrants Dynamic Survey in 2012–2017. The findings indicate that the farmland circulation in the Wuhan urban agglomeration was generally low, but slightly higher than that of the national average. The central city, Wuhan, had a high degree of family migration and social integration, indicating stronger resource flows in developed areas. However, its farmland circulation level was lower than that of non-central cities. The unsynchronized interaction of resources in urban and rural areas should be taken seriously, especially in areas with a relatively developed urban economy. The advantages of the central city in absorbing and settling migrants confirmed the positive impact of the urban agglomeration on promoting urban–rural integration.


2020 ◽  
Vol 12 (16) ◽  
pp. 2514
Author(s):  
Tongwen Li ◽  
Yuan Wang ◽  
Qiangqiang Yuan

Nitrogen dioxide (NO2) is an essential air pollutant related to adverse health effects. A space-time neural network model is developed for the estimation of ground-level NO2 in this study by integrating ground NO2 station measurements, satellite NO2 products, simulation data, and other auxiliary data. Specifically, a geographically and temporally weighted generalized regression neural network (GTW-GRNN) model is used with the advantage to consider the spatiotemporal variations of the relationship between NO2 and influencing factors in a nonlinear neural network framework. The case study across the Wuhan urban agglomeration (WUA), China, indicates that the GTW-GRNN model outperforms the widely used geographically and temporally weighted regression (GTWR), with the site-based cross-validation R2 value increasing by 0.08 (from 0.61 to 0.69). Besides, the comparison between the GTW-GRNN and original global GRNN models shows that considering the spatiotemporal variations in GRNN modeling can boost estimation accuracy. All these results demonstrate that the GTW-GRNN based NO2 estimation framework will be of great use for remote sensing of ground-level NO2 concentrations.


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