Study on City-Level Optimization of Tourism Industry Spatial Organization Nodes and Organization Mode for Tourist Destinations
Reasonable spatial organization of the tourism industry can improve the utilization efficiency of regional tourism industry elements. Taking Dalian City in China as an example, this paper collects various types of tourism industry data and introduces GIS network analysis technology into tourism stud...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/2807817 |
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| _version_ | 1832566628728963072 |
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| author | Jinlian Hao Haolong Liu Jie Chen Fankai Sun |
| author_facet | Jinlian Hao Haolong Liu Jie Chen Fankai Sun |
| author_sort | Jinlian Hao |
| collection | DOAJ |
| description | Reasonable spatial organization of the tourism industry can improve the utilization efficiency of regional tourism industry elements. Taking Dalian City in China as an example, this paper collects various types of tourism industry data and introduces GIS network analysis technology into tourism studies to determine the location, scale, and number of tourism nodes in Dalian and optimize the spatial organization nodes and organization models of the tourism industry. This will help ease the pressure on tourism reception in the southern area of Dalian and promote better development and utilization of tourism resources and tourism facilities in the central and northern regions. The results show that (1) when using the “minimizing facility points” model, a total of 17 second-level tourism nodes and 5 first-level tourism nodes are obtained after optimization. The location of these nodes is highly correlated with the level of tourist scenic spots, while tourist scenic spots play a significant role in leading and driving tourism nodes. (2) Using the “maximum coverage” model for optimization, 3138 tourism enterprises are connected with tourism nodes, thus realizing the shortest traffic path between tourism enterprises and tourism nodes, which minimizes the total cost of network services. Compared with suburban areas, enterprises in urban tourism areas are densely distributed, meaning that a smaller service radius of tourism nodes can cover more enterprises. (3) A total of 10 first-level tourism channels and 12 second-level tourism channels are optimized using the “nearest facility” model. The first-level tourism channels are mainly distributed in the central and southern areas of Dalian. These channels connect nodes mainly through national and provincial roads. The second-level tourism channels are mainly distributed in the central and northern areas of Dalian. (4) This study aims to analyze the evolution process of the spatial organization mode of Dalian’s tourism industry and construct a hub-spoke network tourism industry spatial organization mode composed of 17 hubs, 22 spokes, and 22 tourism domains. The analysis and construction are designed according to the optimization results of tourism nodes and tourism channels. The research results enrich the theories and technical means of tourism industry spatial organization and provide references and suggestions for local governments or tourism planning decision-makers; they also provide a scientific basis for the rational allocation of tourism industry elements and promote the rational distribution of tourism industry. |
| format | Article |
| id | doaj-art-98e7eb0a3fca4ad89e5ebc2f189d8e66 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-98e7eb0a3fca4ad89e5ebc2f189d8e662025-02-03T01:03:40ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/28078172807817Study on City-Level Optimization of Tourism Industry Spatial Organization Nodes and Organization Mode for Tourist DestinationsJinlian Hao0Haolong Liu1Jie Chen2Fankai Sun3College of History and Tourism Culture, Shanxi Datong University, Datong, Shanxi 037009, ChinaKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaCollege of Architecture and Surveying Engineering, Shanxi Datong University, Datong, Shanxi 037009, ChinaDepartment of Geomatics, Taiyuan University of Technology, Taiyuan, Shanxi 030024, ChinaReasonable spatial organization of the tourism industry can improve the utilization efficiency of regional tourism industry elements. Taking Dalian City in China as an example, this paper collects various types of tourism industry data and introduces GIS network analysis technology into tourism studies to determine the location, scale, and number of tourism nodes in Dalian and optimize the spatial organization nodes and organization models of the tourism industry. This will help ease the pressure on tourism reception in the southern area of Dalian and promote better development and utilization of tourism resources and tourism facilities in the central and northern regions. The results show that (1) when using the “minimizing facility points” model, a total of 17 second-level tourism nodes and 5 first-level tourism nodes are obtained after optimization. The location of these nodes is highly correlated with the level of tourist scenic spots, while tourist scenic spots play a significant role in leading and driving tourism nodes. (2) Using the “maximum coverage” model for optimization, 3138 tourism enterprises are connected with tourism nodes, thus realizing the shortest traffic path between tourism enterprises and tourism nodes, which minimizes the total cost of network services. Compared with suburban areas, enterprises in urban tourism areas are densely distributed, meaning that a smaller service radius of tourism nodes can cover more enterprises. (3) A total of 10 first-level tourism channels and 12 second-level tourism channels are optimized using the “nearest facility” model. The first-level tourism channels are mainly distributed in the central and southern areas of Dalian. These channels connect nodes mainly through national and provincial roads. The second-level tourism channels are mainly distributed in the central and northern areas of Dalian. (4) This study aims to analyze the evolution process of the spatial organization mode of Dalian’s tourism industry and construct a hub-spoke network tourism industry spatial organization mode composed of 17 hubs, 22 spokes, and 22 tourism domains. The analysis and construction are designed according to the optimization results of tourism nodes and tourism channels. The research results enrich the theories and technical means of tourism industry spatial organization and provide references and suggestions for local governments or tourism planning decision-makers; they also provide a scientific basis for the rational allocation of tourism industry elements and promote the rational distribution of tourism industry.http://dx.doi.org/10.1155/2020/2807817 |
| spellingShingle | Jinlian Hao Haolong Liu Jie Chen Fankai Sun Study on City-Level Optimization of Tourism Industry Spatial Organization Nodes and Organization Mode for Tourist Destinations Complexity |
| title | Study on City-Level Optimization of Tourism Industry Spatial Organization Nodes and Organization Mode for Tourist Destinations |
| title_full | Study on City-Level Optimization of Tourism Industry Spatial Organization Nodes and Organization Mode for Tourist Destinations |
| title_fullStr | Study on City-Level Optimization of Tourism Industry Spatial Organization Nodes and Organization Mode for Tourist Destinations |
| title_full_unstemmed | Study on City-Level Optimization of Tourism Industry Spatial Organization Nodes and Organization Mode for Tourist Destinations |
| title_short | Study on City-Level Optimization of Tourism Industry Spatial Organization Nodes and Organization Mode for Tourist Destinations |
| title_sort | study on city level optimization of tourism industry spatial organization nodes and organization mode for tourist destinations |
| url | http://dx.doi.org/10.1155/2020/2807817 |
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