Spatial Patterns of Key Villages and Towns of Rural Tourism in China and Their Influencing Factors

Qiuyu Zou, Jianwei Sun (), Jing Luo, Jiaxing Cui and Xuesong Kong
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Qiuyu Zou: School of Geographical and Environmental Sciences, Guizhou Normal University, Guiyang 550025, China
Jianwei Sun: School of Geographical and Environmental Sciences, Guizhou Normal University, Guiyang 550025, China
Jing Luo: Hubei Provincial Key Laboratory for Geographical Process Analysis and Simulation, Central China Normal University, Wuhan 430079, China
Jiaxing Cui: Hubei Provincial Key Laboratory for Geographical Process Analysis and Simulation, Central China Normal University, Wuhan 430079, China
Xuesong Kong: School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China

Sustainability, 2023, vol. 15, issue 18, 1-16

Abstract: This study takes 1597 key villages and towns part of rural tourism in China as research objects and uses the ArcGIS spatial analysis method, combined with the geodetector and the multiscale geographically weighted regression model, to analyze the intensity and spatial differentiation of factors influencing the spatial distribution of these villages and towns. (1) The key villages and towns of rural tourism exhibit clustering distribution patterns with more locations in the east than in the west. The center of gravity of this distribution shifts to the northwest, displaying a “belt–point” trend with scattered hot spots in Beijing, Tianjin, Hebei, and the Yangtze River Delta, while cold spots are primarily concentrated in the northwest. (2) Spatial variation in the key villages and towns of rural tourism is due to multiple factors, of which population density, resident consumption expenditure, and per capita GDP display the highest explanatory powers for the spatial distribution of tourism. (3) The primary influencing factors are spatial differences in their roles and substantial local imbalances. Positive and negative correlation analysis units exhibit the aggregation characteristics of being blocked and banded. These results can provide valuable guidance for the development of rural tourism, promoting its sustainable development and contributing to the revitalization of rural areas.

Keywords: key villages of rural tourism; key towns for rural tourism; spatial distribution; geodetector; multiscale geographically weighted regression (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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