摘要
基于2010—2019年18个产茶省份的茶产业和旅游业统计数据,采用修正的引力模型测算其综合发展水平的关联强度,通过社会网络分析法分析其空间关联网络结构特征。研究发现:(1)18个产茶省份的茶产业与旅游业综合发展水平整体形成了相对稳定的空间关联网络结构,整体空间关联网络存在省际间的关联性和跨区域的传递性,但网络密度还处于较低水平,网络关联关系主要发生在邻近省份之间;(2)近10年,江苏及浙江等省份个体网络的度数中心度、接近中心度和中间中心度较高,处于网络的核心位置,其虹吸效应显著;(3)2019年茶产业与旅游业综合发展水平空间关联网络结构划分为“主受益”板块、“主溢出”板块、“经纪人”板块及“双向溢出”四大板块,四大板块没有形成多向交互的完整传导路径。
Based on the statistical data of tea industry and tourism in 18 tea producing provinces from 2010 to 2019,the correlation of their comprehensive development level was measured by the modified gravity model,and the spatial correlation network structure characteristics were analyzed by the social network analysis method.The research finds that:(1)the comprehensive development level of tea industry and tourism in 18 tea producing provinces had formed a relatively stable spatial correlation network structure.The overall spatial correlation network had inter provincial correlation and trans regional transmission,but the network density was still at a low level,and the network correlation mainly occurred between neighboring provinces;(2)In the past 10 years,the degree centrality,proximity centrality and intermediate centrality of individual networks in Jiangsu and Zhejiang provinces were high,which were at the core of the network,and their siphon effect was significant;(3)In 2019,the spatial correlation network structure of the comprehensive development level of the tea industry and the tourism industry was divided into four major plates,namely,the"main benefit"plate,the"main overflow"plate,the"broker"plate and the"two-way overflow".The four plates did not form a complete transmission path of multi-directional interaction.
作者
张明山
陈晓强
袁申梅
叶慧婷
ZHANG Mingshan;CHEN Xiaoqiang;YUAN Shenmei;YE Huiting(Tourism College,Ji'an College,Ji'an 343000,China;School of Culture and Tourism,Gannan Normal University,Ganzhou 341000,China)
出处
《中国茶叶》
2022年第12期16-23,28,共9页
China Tea
基金
吉安市社会科学规划项目(22GHA292)
吉安职业技术学院人文社科科研项目(20RW205)。
关键词
茶产业
旅游业
社会网络分析法
引力模型
空间关联网络
tea industry
tourism industry
social network analysis
gravity model
spatial correlation network
