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我国半资源消费型野生动物旅游景区时空演变特征及其驱动因素分析
引用本文:丛丽,于佳平,王灵恩.我国半资源消费型野生动物旅游景区时空演变特征及其驱动因素分析[J].自然资源学报,2020,35(12):2831-2847.
作者姓名:丛丽  于佳平  王灵恩
作者单位:1.北京林业大学园林学院旅游管理系,北京 1000832.中山大学旅游学院,广州 5102753.中国科学院地理科学与资源研究所,北京 100101
基金项目:国家自然科学基金项目(41601129);中央高校基本科研业务费专项资金(2015ZCQ-YL-04)
摘    要:选取1906—2019年间我国574个半资源消费型野生动物旅游景区为研究对象,采用地理学的空间分析方法对其时空分布特征、演变规律及驱动因素进行实证分析。结果表明: (1)我国半资源消费型野生动物旅游景区的发展可分为探索(1906—1948年)、快速发展(1949—1959年)、慢速发展(1960—1992年)和高速发展(1993—2019年)四个阶段。(2)空间上呈凝聚型分布,地理集中程度较高,地区间不均衡程度高,形成了以北京、江浙沪交界及广州为核心的三个高密度区;整体呈现东南沿海多,中部过渡,西北内陆少的格局;随着时间的演化,凝聚形态一直延续,地理集中度逐渐下降,地区间的不均衡程度逐渐加深。(3)时空演化的影响因素包括历史背景、国家政策和地方人口数量、经济及旅游发展规模等,呈现了较强的正相关性。

关 键 词:半资源消费型  野生动物旅游  景区  时空分布  演变规律  影响因素  
收稿时间:2019-11-06
修稿时间:2019-12-18

Spatiotemporal evolution and its influencing factors of semi-consumptive wildlife tourist attractions in China
CONG Li,YU Jia-ping,WANG Ling-en.Spatiotemporal evolution and its influencing factors of semi-consumptive wildlife tourist attractions in China[J].Journal of Natural Resources,2020,35(12):2831-2847.
Authors:CONG Li  YU Jia-ping  WANG Ling-en
Institution:1. School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China2. School of Tourism Management, Sun Yat-Sen University, Guangzhou 510275, China3. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
Abstract:The interaction between human beings and wild animals has a profound and complex history. Wildlife tourism has gradually been seen as a compatible medium for people to interact with nature and wild animals. Along with the increasing demand in China, the supply of wildlife-themed tourist attractions, to some extent, has disordered competition, resulting in a waste of resources. Therefore, it is necessary to sort and analyze the resource base of semi-consumptive wildlife tourist attractions in China. Selecting 574 semi-consumptive wildlife tourism attractions in China from 1906 to 2019 as objects of study, this paper analyzed the spatial and temporal distribution, evolution characteristics as well as the related influencing factors of semi-consumptive wildlife tourist attractions in China, using models such as inter-annual variability index, nearest neighbor index, geographic concentration index, the Gini coefficient and the nuclear density analysis. The results show that: (1) In terms of time, the opening time of semi-consumptive wildlife tourist attractions in China is scattered, and can be roughly divided into four stages, namely the exploration stage (1906-1948), rapid development stage (1949-1959), slow development stage (1960-1992) and high-speed development stage (1993-2019). (2) In terms of space, the attractions present a cohesive distribution, and are geographically concentrated. The development between regions is highly unbalanced. Three high-density areas have formed with Beijing, Jiangsu-Zhejiang-Shanghai and Guangzhou as cores. Overall, there are more attractions in the eastern and southern coastal areas and less in the western part of China, with a transition in central China. Over time, the cohesion form continues and geographic concentration index gradually decreases approaching the assumed average level. However, the growth between regions is increasingly unbalanced. (3) The distribution of wildlife tourist attractions is influenced by development of history and national policies concerning the protection of wildlife animals, and has a certain positive correlation with the local factors such as population, the degree of economic development and the scale of tourism development. The attractions tend to be located in areas with great regional advantages, high population density, developed economy and leading tourism development. The research conclusion provides an overall cognition of spatial and temporal distribution of semi-consumptive wildlife attractions in China and will be the scientific basis and reference for investors and relevant managers to promote the further development and planning of wildlife tourism in the future.
Keywords:semi-consumption  wildlife tourism  tourist attractions  spatiotemporal distribution  spatiotemporal evolution  influencing factor  
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