Abstract

The Yangtze River Basin (YRB) is the birthplace of Chinese civilization and is rich in traditional village resources. Studying their spatial distribution characteristics and influencing factors can guide the protection, inheritance, and development of traditional villages in YRB. This study takes 5 batches of 3346 traditional villages in YRB since 2012 as the research object. Using the nearest neighbor index, kernel density analysis, standard deviation ellipse, and Geodetector model, we analyzed the spatial distribution characteristics of traditional villages in YRB and detected their influencing factors. The results show that the distribution of traditional villages in YRB exhibited a spatial pattern of cohesive clusters, forming a high-density area and development center in the junction zone between Guizhou and Hunan provinces and southeast of Anhui Province, and secondary-density areas in Northeast Yunnan Province and east Jiangxi Province. The results of the Geodetector show that the formation of the spatial distribution pattern of traditional villages in YRB is affected by the combined effects of natural and socio-economic factors, among which elevation and NDVI were the main factors, and the interaction of multiple factors showed an enhanced trend. The findings of this study can provide scientific decision-making support for the development and protection of traditional villages in YRB.

Details

Title
Spatio-temporal characteristics and influencing factors of traditional villages in the Yangtze River Basin: a Geodetector model
Author
Chen, Wanxu 1 ; Yang, Liyan 1 ; Wu, Jianhua 1 ; Wu, Jiahui 1 ; Wang, Guanzheng 1 ; Bian, Jiaojiao 1 ; Zeng, Jie 1 ; Liu, Zhiling 2 

 China University of Geosciences, Department of Geography, School of Geography and Information Engineering, Wuhan, People’s Republic of China (GRID:grid.503241.1) (ISNI:0000 0004 1760 9015) 
 China University of Geosciences, School of Public Administration, Wuhan, People’s Republic of China (GRID:grid.503241.1) (ISNI:0000 0004 1760 9015) 
Pages
111
Publication year
2023
Publication date
Dec 2023
Publisher
Springer Nature B.V.
e-ISSN
20507445
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2814634088
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.