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  • 人文与社会
  • 文章编号:1009-6000(2024)07-0124-07
  • 中图分类号:F592    文献标识码:B
  • Doi:10.3969/j.issn.1009-6000.2024.07.018
  • 项目基金:国家自然科学基金面上项目(52178043);中国博士后科学基金资助项目(2022M710669);江苏省自然科学基金面上项目(BK20211371);安徽省文旅创新发展研究院资助课题。
  • 作者简介:韦胜,正高级城乡规划师,博士,江苏省规划设计集团有限公司主任工程师,主要从事城市定量模型研究;张译文,苏州大学金螳螂建筑学院风景园林专业在读本科生,研究方向为风景园林设计;徐海贤,通信作者,教授级城市规划师,博士,江苏省规划设计集团有限公司技术中心总规划师,主要从事区域和城市研究。
  • 南京市游客社会感知特征差异研究
  • Research on the Differences in Tourist Social Perception Characteristics in Nanjing City
  • 韦胜 张译文 徐海贤
  • WEI Sheng ZHANG Yiwen XU Haixian
  • 摘要:
    研究基于自然语言和复杂网络分析技术,利用大众点评的评论数据,对南京市老城区的本地和外地游客的社会感知特征进行分析。研究发现本地和外地游客所重点关注的景点类型存在着一定差异,外地游客所关注景点比本地游客在空间上更为聚集,呈现“三角网”的结构;本地游客所重点关注的景点之间联系网络的空间覆盖范围广;在关注的主题内容上也有所差异,本地游客更偏重于日常旅游问题,而外地游客对南京市的历史、著名景点、建筑等要素更为注重;关键词语义网络分析结果表明本地和外地游客在一些重要景点关联上也存在着差异性。总体上,研究成果可为当前城市旅游发展规划和相关政策制定提供一定的参考依据。
  • 关键词:
    社会感知 ;旅游 ;复杂网络 ;自然语言;随机游走算法
  • Abstract: The study, based on natural language and complex network analysis techniques, utilizes review data from Dianping to analyze the social perception characteristics of local and non-local tourists in the old city of Nanjing. The findings reveal that there are certain differences in the types of attractions that local and non-local tourists focus on. Non-local tourists tend to concentrate more spatially on specific attractions, forming a ¡°triangular network¡± structure, while the network of attractions that local tourists focus on has a broader spatial coverage. There are also differences in the themes of interest: local tourists are more concerned with general issues in everyday tourism, whereas non-local tourists pay more attention to elements such as Nanjing¡¯s history, famous landmarks, and architecture. The results of the keyword semantic network analysis indicate that there are differences in the associations with important attractions between local and non-local tourists. Overall, the research findings can provide some reference for current urban tourism development planning and related policy formulation.
  • Key words: social perception; tourism; complex network; natural language; random walk algorithm
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