Spatial Distribution Characteristics and Influencing Factors of Wellness Tourism Destinations in Hubei Province
Year: 2025
Type: article
Abstract: 随着大健康时代的到来，康养旅游作为一种新兴的旅游模式，其需求不断增长，成为旅游业的重要组成部分。以湖北省219处康养旅游地为研究点，运用GIS空间分析，结合最邻近指数法、Ripley’s K函数、核密度分析法及地理探测器等方法，探究湖北省康养旅游地的空间分布特点及影响因素。研究发现，湖北省康养旅游地资源丰富但分布不均，主要分为六类，其中森林康养地数量最多。在空间结构上，总体上呈聚集分布，不同类型康养旅游地空间结构存在差异。在分布格局上，呈现“M”字形的“三核一带”格局，不同类型康养旅游地空间布局差异大。康养旅游地的空间分布受自然和人为因素综合影响，年降水量对其空间分布的影响力最大，年平均降水与人均可支配收入是对空间分布差异影响最强的交互因子，各因素间交互作用增强影响力。With the advent of the era of great health, wellness tourism, as an emerging tourism model, has seen its demand continuously increase and become an important component of the tourism industry. Taking 219 wellness tourism destinations in Hubei Province as research samples. This study integrates multiple analytical approaches, including GIS spatial analysis, nearest neighbor index, Ripley’s K-function, kernel density estimation, and geographical detector, to systematically investigate the spatial distribution patterns and underlying mechanisms of wellness tourism destinations in Hubei Province. The research reveals that Hubei Province is rich in wellness tourism resources but their distribution is uneven, primarily categorized into six types, with forest wellness destinations being the most numerous. In terms of spatial structure, the destinations generally exhibit an aggregated distribution, with differences observed among the spatial structures of different types of wellness tourism destinations. In terms of distribution pattern, an “M”-shaped “three cores and one belt” pattern emerges. Wellness tourism typologies exhibit marked spatial heterogeneity in their geographical configuration. The spatial manifestation of these destinations demonstrates statistically significant clustering patterns attributable to the synergistic interplay of biophysical and socioeconomic drivers, with annual precipitation having the greatest impact on their spatial distribution. The interaction between annual average precipitation and per capita disposable income is the strongest interactive factor influencing spatial distribution differences, and the interaction among various factors enhances their influence.
Source: Statistics and Applications
Author: 欣 刘
Language: English
Subfield: General Health Professions
Field: Health Professions
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4411832346