地理学报

地理学报 ›› 2021, Vol. 76 ›› Issue (6): 1537-1552.doi: 10.11821/dlxb202106015

• 旅游地理 • 上一篇    下一篇

基于圈层结构的游客活动空间边界提取新方法

吴朝宁1(), 李仁杰1,2, 郭风华3,4   

  1. 1. 河北师范大学资源与环境科学学院,石家庄 050024
    2. 河北省环境演变与生态建设实验室,石家庄 050024
    3. 河北省科学院地理科学研究所,石家庄 050011
    4. 河北省地理信息开发应用工程技术研究中心,石家庄 050011
  • 收稿日期:2019-07-27 修回日期:2020-10-03 出版日期:2021-06-25 发布日期:2021-08-25
  • 作者简介:吴朝宁(1993-), 男, 河北石家庄人, 硕士生, 主要从事地理信息建模研究。E-mail: wzn19930319@163.com
  • 基金资助:
    国家自然科学基金项目(41471127);国家自然科学基金项目(41171105);河北省杰出青年基金培育项目(D2015205208);河北省高层次人才资助项目(A2016001130)

A novel method for extracting spatial boundary of tourist activity based on circle structure

WU Zhaoning1(), LI Renjie1,2, GUO Fenghua3,4   

  1. 1. College of Resources and Environment Science, Hebei Normal University, Shijiazhuang 050024, China
    2. Hebei Key Laboratory of Environmental Change and Ecological Construction, Shijiazhuang 050024, China
    3. Hebei Institute of Geographical Sciences, Shijiazhuang 050011, China
    4. Hebei Engineering Research Center for Geographic Information Application, Shijiazhuang 050011, China
  • Received:2019-07-27 Revised:2020-10-03 Published:2021-06-25 Online:2021-08-25
  • Supported by:
    National Natural Science Foundation of China(41471127);National Natural Science Foundation of China(41171105);Hebei Science Fund of Distinguished Yong Scholars(D2015205208);Foundation for Talent Training Project in Hebei Province(A2016001130)

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摘要：

准确刻画游客活动空间边界对于优化景区结构、实施界限管控、提高资源利用效益均有重要意义。由于游客行为的复杂性与边界模糊性,利用传统地理边界提取方法难以有效识别游客活动空间边界。基于层次聚类算法优化后的Delaunay三角网进行核密度估计,解决了多尺度下点核密度对空间边界拟合不精确的问题,同时借鉴圈层结构理论,依据游客空间集聚特征建立景区层次结构,利用大量游客长时间签到蕴含的时空信息,分析游客空间分布扩张规律,挖掘地理要素关系,建立“Hie-Density”模型,提出基于圈层结构理论的游客活动空间边界定量提取新方法。本文通过微观视角下圈层子系统的协同作用探究主体系统的宏观演化,证明了“Hie-Density”模型支持对多种游客分布模式进行描述,同时能够依据模型变化曲线定量识别游客活动最佳边界、空间集散状态、中心分裂特征及边界演化方向。多案例实证表明,本方法适用于各类景区的多尺度复杂游客活动空间边界提取,为地理时空数据挖掘提供了新视角和新方法。

关键词: 游客活动空间, 边界提取, 圈层结构理论, Hie-Density模型, 核密度

Abstract:

It is of great significance for tourism research to extract the spatial boundary of tourist activity, especially for optimizing the spatial structure of scenic spots, implementing boundary control and improving the utilization efficiency. Due to the complexity of tourist activity, it is difficult to identify the spatial boundary of tourist activity by using traditional method effectively. The study uses optimized Delaunay triangulation based on hierarchical clustering algorithm (ASCDT), instead of the points feature, which is dedicated to solving the problem of inaccurate spatial fitting in kernel density estimation at multi-scale. We analyze the spatial aggregation characteristic of tourist activity, and build the hierarchy based on concentric zone theory. Then we analyze the relations in geographic factors by using lots of spatiotemporal sign-in data, and propose a novel method for extracting the boundary of tourist activity, which is called "Hie-Density". "Hie-Density" is based on the spatial law of the expanding distribution. The study explores the macroscopic system evolution by the interaction of circle subsystems, and proves that "Hie-Density" can be used to discuss the multiple distribution patterns of spatial activity. According to the law of deviation in curve, it is easy to identify the optimal spatial boundary of tourist activity, the state of spatial aggregation, the characteristic of central splitting and the evolution direction of the spatial boundary. The cases show that the method is applicable to the complex spatial distribution structure. The study involves different kinds of scenic spots at multi-scales, so as to provide a new perspective and a novel method for geographical spatiotemporal data mining.

Key words: space of tourist activity, boundary extraction, concentric zone theory, Hie-Density, kernel density