长征沿线红色旅游多尺度空间行为模式挖掘与仿真模拟  

Mining and Simulating of Tourists' Behavior Patterns in Multi-scale Red Tourism along the Long March Route

作　　者：陈佳淇 王胜宏 王圣斌 刘俊[1] CHEN Jiaqi;WANG Shenghong;WANG Shengbin;LIU Jun(Tourism School,Sichuan University,Chengdu 610065,China)

机构地区：[1]四川大学旅游学院,四川成都610065

出　　处：《旅游学刊》2024年第4期124-138,共15页Tourism Tribune

基　　金：四川大学研究基金(SKSYL2022-04);四川大学区域历史与边疆学学科群项目;四川大学教学改革项目(SCU8115);四川省教学改革项目(JG2021-391)共同资助。

摘　　要：旅游者空间行为模式是旅游地理学的重要理论命题。针对长征沿线多尺度红色旅游空间行为的研究,不仅可以丰富地理学在人类闲暇活动空间和行为决策方面的理论,还可以为长征国家文化公园建设的空间结构规划、旅游线路优化等实践需求提供支撑。文章获取了2012—2022年长征沿线红色旅游轨迹1 576 093条,运用无监督学习模型分别从大、中、小尺度挖掘红色旅游者空间行为模式,通过仿真模拟分析评估模式韧性。主要结论有:1)长征沿线红色旅游活动尺度越大模式越少,大、中、小尺度下分别形成了17种、56种和81种红色旅游模式;2)长征沿线已形成8个主要的联动区域,大尺度下主要为2~4个省份间的联动,中尺度下以3~6个城市间的联动为主,小尺度下则表现为1~3个长征相关红色旅游景区联动特征;3)模式网络韧性来看,中尺度模式>小尺度模式>大尺度模式,中小尺度的模式间韧性差异更大。Tourists' spatial behavior pattern is a key theoretical issue in tourism management.Amid China's efforts to construct the Long March National Cultural Park,scientifically integrating red tourism resources,mastering the situation of regional integration and understanding the red tourism behavior pattern holds the key to high-quality development of red tourism along the Long March route.The exploration of tourists' behavior patterns in multi-scale red tourism can enrich the theoretical research in geography on human leisure activity spaces and decision-making behaviors and offer support for meeting the practical needs of spatial planning and tourism route optimization in building the Long March National Cultural Park.Therefore,there is a pressing need for the academic community to conduct pertinent studies in light of the current scenario.The spatiotemporal big data generated by tourists brings new opportunities for research on their spatiotemporal behaviors.Compared with traditional data collection methods,spatial and temporal data utilizing various positioning technologies can be acquired in a shorter time,in larger quantities and with higher precision.It is worth noting that the analysis methods of tourism spatiotemporal behaviors developed based on traditional data fail to meet the demand for big data analysis.Although interdisciplinary research methods such as deep learning,statistical physics,and complex networks provide brand-new research tools for the study of tourists' behaviors,the adoption of cutting-edge technologies in tourism research still needs further exploration and expansion.Developing big data analysis methods applicable to tourism scenarios that incorporate the characteristics of tourism big data,reviewing existing scientific questions,and asking and answering new ones are gradually becoming an important part of tourist behavior study.To create a dataset of red tourist scenic spots and trajectories along the Long March route,this study acquired 1 576 093 tourism GPS trajectories from 2012 to 20

关 键 词：空间行为模式 模式网络韧性 红色旅游 长征 无监督学习 

分 类 号：F59[经济管理—旅游管理]