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马益鹏, 丛丽, 钱皓月. 基于网络文本分析的大熊猫国家公园拥挤度感知影响因素及调适行为研究[J]. 北京林业大学学报(社会科学版), 2019, 18(1): 108-117. DOI: 10.13931/j.cnki.bjfuss.2018144
引用本文: 马益鹏, 丛丽, 钱皓月. 基于网络文本分析的大熊猫国家公园拥挤度感知影响因素及调适行为研究[J]. 北京林业大学学报(社会科学版), 2019, 18(1): 108-117. DOI: 10.13931/j.cnki.bjfuss.2018144
MA Yi-peng, CONG Li, QIAN hao-yue. Factors Influencing Congestion Perception and Adjustment Behavior in Giant Panda National Park Based on Network Text Analysis[J]. Journal of Beijing Forestry University (Social Science), 2019, 18(1): 108-117. DOI: 10.13931/j.cnki.bjfuss.2018144
Citation: MA Yi-peng, CONG Li, QIAN hao-yue. Factors Influencing Congestion Perception and Adjustment Behavior in Giant Panda National Park Based on Network Text Analysis[J]. Journal of Beijing Forestry University (Social Science), 2019, 18(1): 108-117. DOI: 10.13931/j.cnki.bjfuss.2018144

基于网络文本分析的大熊猫国家公园拥挤度感知影响因素及调适行为研究

Factors Influencing Congestion Perception and Adjustment Behavior in Giant Panda National Park Based on Network Text Analysis

  • 摘要: 大熊猫国家公园试点工作面临着栖息地割裂下的野生动物保护和国内日益高涨的旅游需求等多重挑战。基于大数据网络文本,以大熊猫国家公园为案例地,分析旅游者拥挤度感知影响因素及调适行为。选取大熊猫国家公园中已开展游憩活动且富有代表性的成都大熊猫繁育研究基地、碧峰峡和王朗自然保护区3处采集网络评论文本数据,文本时间跨度为2011—2018年,共包含1711条评论,共计132354个字,运用ROST Content Mining 6计算机分析软件和NVivo 8质性分析软件,结合内容分析法和质性主题分析法,对拥挤度感知相关文本进行了分析。研究发现,旅游者个体及出游特征、情景特征和人口密度是影响旅游者拥挤感知的3个重要因素,调适响应作为对拥挤状况的反馈行为,是特殊的拥挤度感知影响因素,也是评论文本的重要内容。最后,基于对大熊猫国家公园游客的深入分析,针对国家公园类型旅游目的地的拥挤问题管理提出了相关建议,以促进国家公园更好地实现野生动物保护和目的地游憩管理的双重目标。

     

    Abstract: The pilot work of the Giant Panda National Park faces various challenges such as the protection of wildlife under habitat fragmentation and the increasing domestic demand for tourism. Based on the network text analysis method, the Giant Panda National Park is taken as a case to analyze the influencing factors and adaptation behavior of tourists' congestion perception. We collect online texts of the Chengdu Giant Panda Breeding Base, Bifengxia and Wanglang Nature Reserves, which are representative and often carry out recreation activities in the Giant Panda National Park, and by using ROST Content Mining 6 computer analysis software and NVivo 8 qualitative analysis software, combined with content analysis and qualitative subject analysis, we analyzed the crowded perception-related text. The text data covers 1711 comments from 2011 to 2018, for a total of 132354 words. The study found that the individual and travel characteristics, scene characteristics and population density of tourists are three important factors affecting the crowd's perception of congestion. As a feedback behavior to the crowded situation, the adaptation response is a special factor influencing congestion perception and an important part of the comment text. Finally, based on the in-depth analysis of tourists in the Giant Panda National Park, relevant suggestions for handling the problem of crowding in national park type tourism destinations are put forward, to promote the national park to achieve the dual goals of wildlife protection and destination recreation management.

     

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