Measuring Spatial Patterns of Tea Farms and Their Colocation Relationships with Agritourism Scenic Spots from Geo-Spatial Datasets in Huzhou, China

The tea industry is a mainstay in agricultural sector in Zhejiang Province, China. Huzhou City, a well-known rural tourism destination in the northern part of the Province, holds significant historical importance as the birthplace of world tea culture. It is also recognized as one of the key regions for Chinese green tea production, boasting a large number of tea plantation bases (tea farms). The purpose of this research is two-fold. First, it extracts data of tea farms from electronic map and creates datasets in a Geographic Information System to quantitatively analyze the overall spatial distribution and characteristics of tea farms in the study area. Second, the study investigates the spatial colocation between tea farm point features and agritourism scenic spot point features to discover whether a spatial (in)dependence exists between the two point classes. Spatial colocation is a fundamental topic in geographic information research. However, there is scarcity of scholarly work that investigates this phenomenon in the contexts of agritourism. In this study, the concept of spatial colocation not only serves as a reference for understanding tea landscape but also signifies a transformation of land use from agricultural industry to the tertiary industry. As rural areas are greatly influenced by road networks, which impact tea production, transportation, and tourism accessibility, this study employs the cross K-function based on the road network in SANET and R language programming to calculate and visualize the spatial proximity between the two sets of points. The network based measurement is more accurate compared to the classic planar K-function for it considers the actual geographical surface rather than the Euclidean distance. A key aspect of this study lies in the utilization of multi-source data for geo-spatial analysis on a GIS platform, along with the application of information software technologies to facilitate relevant calculations and visualizations of the study objects. This study contributes to the existing literature on tea farms in the context of agricultural tourism. The findings provide in-depth insight for guiding the spatial layout of tea farms and informing the landscape and land-use planning in the study area. Moreover, the practical implications of these results can be extended to the decision-making process of spatial planning strategies for agritourism regions.