Characterizing the Spatiotemporal Patterns and Key Determinants of Homestay Industry Agglomeration in Rural China Using Multi Geospatial Datasets

Abstract

Understanding the spatiotemporal patterns and key determinants of rural homestay industry agglomeration is crucial for the well-planning and well-management of rural tourism during the process of rural revitalization in China. By employing multi geospatial datasets, this study in-vestigated the long-term spatiotemporal patterns and their key determinants of homestay inns during the period 2004–2019 in Moganshan, a well-known rural tourism destination in Zhejiang Prov-ince, China. The kernel density estimation and spatial autocorrelation were integrated to identify the hotspots of rural homestay inns at a fine scale. The key determinants were further uncovered using multiple stepwise regression and logistic regression models. The result shows that the overall growth of homestay inns was slow at the early stage and has progressed rapidly since 2014, with 94.2% of homestay inns newly opened during the period 2014–2019. The first hotspot was located in Moganshan National Park and then spread to the surrounding villages. Three hotspot zones have emerged, including the northern hotspot zone (Sihe-Xiantan), central hotspot zone (Houwu-Park-Liaoyuan), and southern hotspot zone (Ziling-Laoling-Lanshukeng) by 2019. The modeling indi-cates that government policy was an essential determinant for the increase in homestay inns, fol-lowed by entrepreneurship and investment. The new homestay inns were more likely to occur in settlements close to scenic spots, river networks, and cultivated land. Abundant scenic spots and heterogeneous landscapes were also preferred when selecting sites and executing landscape design for homestay inns. Our empirical study has provided practical insights for policy makers, entrepre-neurs, and planners for future sustainable homestay industry development.