Spatial location differentiation and development decision optimization of characteristic villages and towns in China

Abstract

Due to rapid urbanization and industrialization, the gap between urban and rural development has gradually increased. Rural development problems have been a significant topic of discussion, and are related to people's livelihoods. This article built a point-axis-region location driving system to analyze the spatial location differentiation of characteristic villages and towns (CVTS) using the kernel density model, and explored the mechanism of location driving factors with a geographical detector model. The results show that vegetables and fruits are the main types of products in CVTS. They account for 27.60% and 34.68% of all types of products, and occur mainly in the east and central regions of China. Moreover, all point-axis-region driving factors have a significant influence on grain crops. The mean values of driving forces of vegetables and fruits are larger than other types of CVTS, and their values are 0.12 and 0.11. The average driving forces on all CVTS in the northeast are higher than those in other regions, especially the driving forces of vegetables and medicinal crops (0.24 and 0.18, respectively). Finally, we proposed that the Chinese government should employ engineering technology, invest on road networks, e-commerce and blockchain technology to optimize the point-axis-region location advantages, to promote the sustainable development of CVTS. The detection of driving mechanisms on spatial location differentiation of CVTS has important research value for location theory and rural region systems research.

Keywords

Characteristic villages and towns / Point-axis-region driving system / Spatial driving factors / Decision optimization / Kernel density

Declaration of Interest Statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This research was Funded by National Natural Science Foundation of China (Grant No. 42001192), China Postdoctoral Science Foundation (Grant No. 2019M662329), MOE Project of Humanities and Social Sciences (Grant No. 20YJCZH070) and the Youth Team Project of Shandong University (Grant No. IFYT20004).

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