中国旅游圈空间格局的ABS分析
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摘要
旅游圈(Tourism Destination Circle)是区域旅游合作的产物和关键概念,对其形成机制和空间格局研究将能够从整体上优化区域旅游资源配置,提升中国旅游业发展水平。虽然大量的区域旅游圈被提出和讨论,但缺乏在全国尺度上的研究,难以从宏观上把握中国区域旅游合作的全貌。此外,传统的旅游圈格局分析多为定性判别,缺乏定量研究,因此需要引入新的研究手段。本研究提出采用基于Agent模拟(Agent-Based Simulation,ABS)的方法,构建以游客旅游景区选择和旅游线路组织为基础的模拟模型,并在Repast仿真平台的上采用Java语言开发相应的旅游圈模拟系统(TDCSS, Tourism Destination Circle Simulation System),实现对中国旅游圈空间格局的模拟分析。
文章首先分析了当前旅游圈问题研究的不足,指出需要采取ABS方法对旅游圈空间格局进行研究。然后分别从旅游圈环境构建、旅游Agent设计以及行为规则设计等三个方面,对旅游圈ABS模型进行了设计研究。其中在对游客Agent进行设计时构建了出游时间、空间分配以及旅游边际效用递减等模型。在行为规则设计里提出采用贪婪法进行旅游景区选择,遗传算法进行最优旅游线路组织。
其次,本文对当前比较流行的ABS模拟平台(Swarm、StarLogo、NetLogo、Repast),进行比较分析,提出采用Repast J+Eclipse作为TDCSS开发环境。
最后,结合我国区域旅游统计数据,我们对不同时间约束条件下、不同交通环境下以及不同的游客行为条件下的旅游圈空间格局进行模拟。并构建了地级市旅游域交互次数和地级市旅游域交互指数两个识别旅游圈的指标,在全国范围内识别出18个主要的旅游圈(东北旅游圈；辽宁省旅游圈；“京津冀”旅游圈；山东省旅游圈；中部旅游圈(河南)；“鄂西”旅游圈；四川省旅游圈；“长三角”旅游圈；湖南省旅游圈；云南省旅游圈；“北部湾”旅游圈；“珠三角”旅游圈；贵州省旅游圈；“闽东南”旅游圈；陕西省旅游圈；新疆省旅游圈；内蒙古旅游圈和甘肃东部旅游圈),文章还对主要的旅游圈的空间位置、空间范围、内部结构进行了分析。结合模拟的结果论文分析了影响我国旅游圈空间结构与规模的条件,并总结了我国旅游圈的空间结构与特点。
Tourism Destination Circle (TDC) is the output of regional tourism cooperation. The research of TDC formulation mechanism and spatial pattern will help to optimize the allocation of tourism resources and promote the level of tourism development. With the warming of regional tourism cooperation, the research of TDC becomes a hot spot of Tourism Geography. Although there is a common view of the definition of TDC, the scale and scope of TDC is still unclear. By far, there are tens of TDC have been raised, but many of them are overlapping construction. Therefore, it is necessary to build an identification criterion which could be used to make division of the national TDCs. Moreover, analysis indicates that it is hard to use traditional mathematical statistics model to investigate TDC patterns. This paper constructs tourist scenic spot selection and tourist route organization models based on the Agent Based Simulation (ABS) method and makes simulation analysis to China TDC patterns using TDCSS (Tourism Destination Circle Simulation System), which is developed on the basis of Repast and Java programming language.
Firstly, the paper points out the problems among current TDC researches and puts forward to use ABS method to study the TDC pattern. Furthermore, the paper makes ABS model designation of TDC on the basis of the tourism environment construction, tourist agent design and the conduction of tourism rules. In addition, the paper constructs travel time model, space allocation model and tourism diminishing marginal utility model while designing tourist agent. The paper uses Greedy Selection method to select tourism scenic spots and Genetic Algorithm method to make optimal tours organization.
Secondly, the paper conducts comparative analysis about the current popular simulation platform (Swarm, Star Logo, NetLogo and Repast) and points out that Repast J and Eclipse will be adopted as development environment.
Finally, the paper makes three main situation simulations (different time constraint, different traffic condition and different visitor behavior) combining with collected data of regional tourism statistics. What is more is that the paper promotes two TDC identification criterions (tourism domain interaction time and tourism domain interaction index) and identifies seventeen main TDCs. They are Northeast TDC, Liaoning TDC, "Jingjinji" TDC, Shandong Province TDC, Middle China TDC (Henan Province), "Chongqing-Exi" TDC, Sichuan Province TDC, Changjiang Delta TDC, Hunan Province TDC, Yunnan Province TDC, "Beibuwan" TDC, Zhujiang Delta TDC, Southeast Fujian Province TDC, Shanxi Province TDC, Xinjiang TDC, Inner Mongolia TDC, East Gansu Province TDC. In addition, the paper conducts separate analysis of the main TDCs'spatial location, special scale and inner structure and analyzes the ingredients which affect the TDC's spatial structure and scale.
文章首先分析了当前旅游圈问题研究的不足,指出需要采取ABS方法对旅游圈空间格局进行研究。然后分别从旅游圈环境构建、旅游Agent设计以及行为规则设计等三个方面,对旅游圈ABS模型进行了设计研究。其中在对游客Agent进行设计时构建了出游时间、空间分配以及旅游边际效用递减等模型。在行为规则设计里提出采用贪婪法进行旅游景区选择,遗传算法进行最优旅游线路组织。
其次,本文对当前比较流行的ABS模拟平台(Swarm、StarLogo、NetLogo、Repast),进行比较分析,提出采用Repast J+Eclipse作为TDCSS开发环境。
最后,结合我国区域旅游统计数据,我们对不同时间约束条件下、不同交通环境下以及不同的游客行为条件下的旅游圈空间格局进行模拟。并构建了地级市旅游域交互次数和地级市旅游域交互指数两个识别旅游圈的指标,在全国范围内识别出18个主要的旅游圈(东北旅游圈；辽宁省旅游圈；“京津冀”旅游圈；山东省旅游圈；中部旅游圈(河南)；“鄂西”旅游圈；四川省旅游圈；“长三角”旅游圈；湖南省旅游圈；云南省旅游圈；“北部湾”旅游圈；“珠三角”旅游圈；贵州省旅游圈；“闽东南”旅游圈；陕西省旅游圈；新疆省旅游圈；内蒙古旅游圈和甘肃东部旅游圈),文章还对主要的旅游圈的空间位置、空间范围、内部结构进行了分析。结合模拟的结果论文分析了影响我国旅游圈空间结构与规模的条件,并总结了我国旅游圈的空间结构与特点。
Tourism Destination Circle (TDC) is the output of regional tourism cooperation. The research of TDC formulation mechanism and spatial pattern will help to optimize the allocation of tourism resources and promote the level of tourism development. With the warming of regional tourism cooperation, the research of TDC becomes a hot spot of Tourism Geography. Although there is a common view of the definition of TDC, the scale and scope of TDC is still unclear. By far, there are tens of TDC have been raised, but many of them are overlapping construction. Therefore, it is necessary to build an identification criterion which could be used to make division of the national TDCs. Moreover, analysis indicates that it is hard to use traditional mathematical statistics model to investigate TDC patterns. This paper constructs tourist scenic spot selection and tourist route organization models based on the Agent Based Simulation (ABS) method and makes simulation analysis to China TDC patterns using TDCSS (Tourism Destination Circle Simulation System), which is developed on the basis of Repast and Java programming language.
Firstly, the paper points out the problems among current TDC researches and puts forward to use ABS method to study the TDC pattern. Furthermore, the paper makes ABS model designation of TDC on the basis of the tourism environment construction, tourist agent design and the conduction of tourism rules. In addition, the paper constructs travel time model, space allocation model and tourism diminishing marginal utility model while designing tourist agent. The paper uses Greedy Selection method to select tourism scenic spots and Genetic Algorithm method to make optimal tours organization.
Secondly, the paper conducts comparative analysis about the current popular simulation platform (Swarm, Star Logo, NetLogo and Repast) and points out that Repast J and Eclipse will be adopted as development environment.
Finally, the paper makes three main situation simulations (different time constraint, different traffic condition and different visitor behavior) combining with collected data of regional tourism statistics. What is more is that the paper promotes two TDC identification criterions (tourism domain interaction time and tourism domain interaction index) and identifies seventeen main TDCs. They are Northeast TDC, Liaoning TDC, "Jingjinji" TDC, Shandong Province TDC, Middle China TDC (Henan Province), "Chongqing-Exi" TDC, Sichuan Province TDC, Changjiang Delta TDC, Hunan Province TDC, Yunnan Province TDC, "Beibuwan" TDC, Zhujiang Delta TDC, Southeast Fujian Province TDC, Shanxi Province TDC, Xinjiang TDC, Inner Mongolia TDC, East Gansu Province TDC. In addition, the paper conducts separate analysis of the main TDCs'spatial location, special scale and inner structure and analyzes the ingredients which affect the TDC's spatial structure and scale.
引文
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