地理学报 ›› 2017, Vol. 72 ›› Issue (10): 1886-1903.doi: 10.11821/dlxb201710012

• 旅游地理 • 上一篇    下一篇

基于机器学习的南京市旅游地个性及其文化景观表征

张郴1,2(), 黄震方1,2(), 张捷3, 葛军莲1,2   

  1. 1. 南京师范大学地理科学学院,南京 210023
    2. 江苏省地理信息资源开发与利用协同创新中心,南京 210023
    3. 南京大学地理与海洋科学学院,南京 210093
  • 收稿日期:2016-10-16 修回日期:2017-02-17 出版日期:2017-11-10 发布日期:2017-11-06
  • 作者简介:

    作者简介:张郴(1982-), 女, 江苏南京人, 博士, 讲师, 研究领域为旅游地理与旅游规划。E-mail:zhangc@njnu.edu.cn

  • 基金资助:
    国家自然科学基金项目(41301145, 41671137);国家旅游局青年专家培养计划(TYETP201526);江苏高校优势学科建设工程资助项目

Urban tourism destination personality and its cultural landscape representation via machine learning: A case study of Nanjing

Chen ZHANG1,2(), Zhenfang HUANG1,2(), Jie ZHANG3, Junlian GE1,2   

  1. 1. School of Geography Science, Nanjing Normal University, Nanjing 210023, China
    2. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
    3. Department of Land Resources and Tourism Sciences, Nanjing University, Nanjing 210093, China
  • Received:2016-10-16 Revised:2017-02-17 Online:2017-11-10 Published:2017-11-06
  • Supported by:
    National Natural Science Foundation of China, No.41301145, No.41671137;Tourism Young Expert Training Program, No.TYETP201526;Priority Academic Program Development of Jiangsu Higher Education Institutions

摘要:

旅游地具有物质和精神双重属性。鉴于旅游地精神属性在量化表达和测量方面的困难,以往旅游地研究较少涉及。旅游地个性概念的提出为旅游地精神属性研究打开了思路。由于开展时间不长,当前旅游地个性研究大多基于营销学中的品牌个性研究思路展开,缺乏对地方适用性的考量,且尚未深入到对个性表征形式及表征机制的探讨。基于此,从地理学视角出发,选取城市旅游地作为研究对象,着眼地方特性,系统构建城市旅游地个性量表,在此基础上,开展针对案例地城市南京的个性测量和分析,并以城市文化景观作为表征媒介,深入探究城市旅游地个性的景观表征形式及表征机制。研究发现:城市旅游地个性主要包含态度、气质、性格、能力四大特征;城市文化景观是城市旅游地个性的重要表征媒介,包含现代空间景观、传统空间景观、生态景观、生活景观、社会景观五大维度;以上景观维度及其所辖景观元素在表征城市旅游地个性方面发挥不同作用。研究过程及结论为包括旅游地在内的地方精神属性研究提供了一种概念框架和方法体系,是对精神层面人地关系认识上的丰富。机器学习这一智能化探索型数据分析手段的运用为处理地理学中普遍存在的高维非线性噪声数据提供了有力的方法支撑。

关键词: 地方精神属性, 城市旅游地个性, 文化景观表征, 机器学习, 南京市

Abstract:

Tourism destination has both material and spiritual attributes. Compared to the material attributes of place, the spiritual attributes are more abstract, which leads to the difficulty in description and measurement for scholars. This may be the main reason for the scarcity of research in this direction. Ekinci and Hosany proposed the concept of 'tourism destination personality' and used Aaker's brand personality scale to measure destination personalities that can be viewed as a pioneering work for investigating spiritual aspects of tourism destination. However, as a new direction, most research on tourism destination personality has followed the way of brand personality research, and moreover, it has not touched some in-depth questions such as the representation and its mechanism referring to tourism destination personality. In view of this, taking urban tourism destination as an example, this paper aims to design a personality scale for urban tourism destination, and to find urban tourism destination personality traits and their landscape representation mechanism. Nanjing is selected as the case in this study. A questionnaire survey was conducted in 2015, and 789 valid questionnaires were finally collected. Machine learning, as an intelligent data analysis tool, is used in this study. By analyzing the collected data, it can be concluded that: (1) Urban tourism destination personalities can be divided into four dimensions, such as 'attitude', 'glamour', 'disposition' and 'capability'. (2) Urban cultural landscapes are important in representing urban destination personalities, and can be divided into five dimensions, i.e., 'modern space landscape', 'traditional space landscape', 'ecological landscape', 'social landscape' and 'landscape for living'. (3) The effects of urban cultural landscapes in representing urban tourism personalities are quite different. This study provides a conceptual framework and method for tourism destination personality research. The findings provide new insights for human-environment interaction from the spiritual perspectives.

Key words: spiritual attribute of place, urban tourism destination personality, cultural landscape representation, machine learning, Nanjing