Multi-Source Spatio-Temporal Data-Based Tourism Structure Analysis of Demonstration City for Global Tourism: Case Study of Liyang, China
Abstract
:1. Introduction
2. Analytical Framework
2.1. Measurement of Dynamic Thermal Status of Tourist Areas
2.2. Measurement of Tourism Area Density Analysis
2.3. Measurement of Regional Tourism Flow Distribution Characteristics
2.4. Empirical Strategies
3. Case Study
3.1. Study Area
3.2. Data Sources
3.2.1. Regional Government Planning Data
3.2.2. Mobile Terminal Background Check-in Data
3.2.3. Land Use Data
4. Results
4.1. Dynamic Thermal Status Results of Tourist Areas
4.2. Density Results of Tourist Areas
4.3. Regional Tourism Flow Distribution Characteristics
4.3.1. Regional Tourism Flow Time Distribution Characteristics
4.3.2. Regional Tourism Flow Spatial Distribution Characteristics
4.3.3. Regional Tourism Flow Effect Distribution Characteristics
5. Discussion
5.1. Is There a Healthy Trend towards Sustainable Regional Tourism Development?
5.2. How Can Truely Global Tourism Be Achieved?
5.3. Can This Study Be Applied to Other Similar Cities?
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Author (Year), Study Area | Data Source and Method | Advantages | Disadvantages |
---|---|---|---|
[33] London, UK | Web-based open source multi-genre data | Study using multiple types of data for the first time. Objective and realistic analysis. | The cost of data collection is too high to promote its use. |
[34] Six cities, Italy | Mobile Wi-Fi location data | A detailed analysis of the dynamic thermal can be carried out on a macro-scale. 9i | Subjective with a human starting point. Some Wi-Fi access points (e.g., private Wi-Fi) are excluded and some groups (e.g., people who do not use mobile phones) are ignored. |
[35] Tehran, Iran | Questionnaire and linear regression analysis methods | The factors influencing dynamic thermal status can be analyzed in terms of traffic. | Traffic represents only one particular aspect of heat. Traffic data are not free from their inherent biases. |
[30,36] U.S. and Singapore | Google Street View Images, Expert scoring and GIS | Data are easy to access and are a powerful way of reflecting the thermal status of the city. | Only the area during the departure and arrival of the Street View camera can be captured and the data are not accurate. |
[37]China | Big data | Four dimensions—national, Yangtze River Delta, city, and street space—are used to complete the theoretical framework for urban thermal analysis. | Parts of the data are not disclosed to the public and access to the dataset is difficult. |
[38] Shanghai, China | Baidu map Heat map | Helps overcome the ambiguity of spatio-temporal data by moving from a ‘time-space’ perspective to a point of view. | The intuitiveness of spatial perspective analysis is ignored. |
[39] Shanghai, China | Commercial facility building base volumes, cellular signaling data and POI data | The spatial resources are evaluated in different dimensions and the results can be used to facilitate urban planning. | It is difficult to obtain this dataset for the building base of commercial facilities and the dimensional selection is small. |
[40] Beijing and Guangzhou, China | POI data obtained by open API services of Dianping | Data are easy to access and are a powerful way of reflecting the urban pattern. | Unable to take into account the contribution of each point to the heat. |
Types of Clustering Algorithms | Major Clustering Algorithm Models | Advantages and Disadvantages |
---|---|---|
Delineated clustering algorithm | K-means Algorithm, K-medoids Algorithm, Expectation Maximization Algorithm [44] | Easy to understand and implement. |
Hierarchical clustering algorithms | BIRCH Algorithm, CURE algorithm, CHAMELEON Algorithm [45,46,47] | Less flexible; need to be used in conjunction with other methods. |
Density clustering algorithm | DPC Algorithm, DBSCAN Algorithm, OPTICS Algorithm, DENCLUE Algorithm [48,49,50,51] | High accuracy; any shape of clustering can be found. |
Grid clustering algorithm | STING Algorithm, CLIQUE Algorithm, WAVE-CLUSTER Algorithm [52,53,54] | Fast processing speed; low volume of data. |
Model clustering algorithms | COBWEB Algorithm, Competitive Learning Algorithm [43,55] | Selected results are representative. |
Other clustering algorithms | FCM Algorithm, Quantum Algorithm, SVM Algorithm, etc. [43] | Immature; low feasibility. |
Area Names | Positioning | Development Ideas |
---|---|---|
North Area | Meditation Eco Resort | Waya Mountain as the core, Jizo culture and meditation culture as the characteristics, leisure sports system construction as the focus |
South Area | Bamboo Forest Tea Village Wellness Resort | Relying on the mountains, water, bamboo, forest, hot springs, tea country, etc., in the Nanshan area tourism elements, implanting Deshou culture |
West Area | Longevity Slow City Resort | Create an international slow city with local characteristics that integrates ecology, culture, and recuperation |
East Area | Water leisure experience Resort | Relying on the Changdang Lake National Wetland Park and the Chinese Ape Geopark, forming a three-dimensional tour space of water, land and air |
Tianmu Lake Area | Liyang City Card | Using Tianmu Lake tourism resort resources as a basis to create a city tourism brand Using Tianmu Lake tourism resort resources as a basis to create a city tourism brand |
Central Area | Tourism Auxiliary Service Center | Providing tourism services such as catering, accommodation, transportation, entertainment and shopping, and undertaking functions such as tourism information consultation in Liyang. |
Year | Planning Policy | Policy Content |
---|---|---|
2016–2030 | Liyang Municipal Comprehensive Transportation Planning | Developing diversified urban transportation system and building a global tourism leisure corridor. |
2016–2030 | Liyang Municipal Tourism Traffic Planning | Planning landscape paths and tourist public transport to establish internal and external tourist transport organization and promote the development of global tourism in a comprehensive manner. |
2016–2030 | Liyang Tourism Planning | Use road networks to connect tourism resources across the region, linking the various tourism sub-regions, with ‘global tourism’ as the core, realizing the scenery across the city and strengthening the global pattern. |
2016–2030 | Liyang Municipal Industry Layout Planning | Accelerating industrial transformation, focusing on creating leisure and vacation tourism, and emphasizing the integration of development among the three industries. The emphasis is on creating leisure and holiday tourism and promoting the development of supporting services for global tourism. |
Number | Coordinate | location Names | Number of Raster Points |
---|---|---|---|
1 | 119°25′48′′ E, 31°17′24′′ N | Tianmu Lake | 26,757 |
2 | 119°31′12′′ E, 31°10′48′′ N | Nanshan Zhuhai | 15,783 |
3 | 119°18′0′′ E, 31°39′0′′ N | Wawu Shan | 9856 |
4 | 119°12′0′′ E, 31°31′48′′ N | Qicai Caoshan | 10,674 |
5 | 119°31′12′′ E, 31°29′24′′ N | Shihou Temple | 7325 |
6 | 119°30′0′′ E, 31°25′12′′ N | Gao Jing Yuan | 5639 |
Area Name | 2018 (Million People) | 2019 (Million People) | Total (Million People) |
---|---|---|---|
Tianmu Lake Area | 1379.5 | 1551.94 | 2931.44 |
South Area | 212.8 | 231.95 | 444.75 |
North Area | 46.88 | 81.83 | 128.71 |
West Area | 53.76 | 97.36 | 151.12 |
East Area | 42.27 | 71.61 | 113.88 |
Central Area | 36.6 | 68.45 | 105.05 |
Area Name | 2018 Spring | 2018 Summer | 2018 Autumn | 2018 Winter | 2019 Spring | 2019 Summer | 2019 Autumn | 2019 Winter |
---|---|---|---|---|---|---|---|---|
Tianmu Lake Area | 352.96 | 420.67 | 368.1 | 237.77 | 391.66 | 473.78 | 418.7 | 267.8 |
South Area | 53.5 | 54.42 | 76.99 | 27.89 | 57.06 | 61.57 | 82.43 | 30.89 |
North Area | 10.72 | 11.67 | 14.08 | 10.41 | 16.04 | 26.62 | 22.76 | 16.41 |
West Area | 14.1 | 13.94 | 13.35 | 12.37 | 22.24 | 30.84 | 25.23 | 19.05 |
East Area | 13.18 | 7.75 | 11.01 | 10.33 | 19.8 | 16.24 | 21.37 | 14.2 |
Central Area | 11.91 | 8.57 | 8.02 | 8.1 | 24.47 | 13.13 | 14.96 | 15.89 |
Area/Month | Tianmu Lake Area | South Area | North Area | West Area | East Area | Central Area |
---|---|---|---|---|---|---|
January | 100.24 | 10.22 | 3.56 | 3.77 | 3.79 | 3.07 |
February | 71.19 | 8.69 | 2.98 | 3.12 | 2.56 | 2 |
March | 116.78 | 10.45 | 3.46 | 3.56 | 4.08 | 3.78 |
April | 117.56 | 10.38 | 3.25 | 3.55 | 4.12 | 3.99 |
May | 118.62 | 32.67 | 4.01 | 6.99 | 4.98 | 4.14 |
June | 73.77 | 9.19 | 2.68 | 2.96 | 1.34 | 1.99 |
July | 126.34 | 20.68 | 4.66 | 3.44 | 1.07 | 2.56 |
August | 220.56 | 24.55 | 4.33 | 7.54 | 5.34 | 4.02 |
September | 59.52 | 8.66 | 3.54 | 2.3 | 2.34 | 1.77 |
October | 255.87 | 59.66 | 6.99 | 8.65 | 6.77 | 4.68 |
November | 52.71 | 8.67 | 3.55 | 2.4 | 1.9 | 1.57 |
December | 66.34 | 8.98 | 3.87 | 5.48 | 3.98 | 3.03 |
Area/Month | Tianmu Lake Area | South Area | North Area | West Area | East Area | Central Area |
---|---|---|---|---|---|---|
January | 116.19 | 12.68 | 6.13 | 6.53 | 5.03 | 6.43 |
February | 78.06 | 9.01 | 3.52 | 4.25 | 3.32 | 4.03 |
March | 126.78 | 11.12 | 5.13 | 5.91 | 4.95 | 6.76 |
April | 131.12 | 10.84 | 4.32 | 5.11 | 5.4 | 7.98 |
May | 133.76 | 35.1 | 6.59 | 11.22 | 9.45 | 9.73 |
June | 86.67 | 9.42 | 5.69 | 6.63 | 3.35 | 2.01 |
July | 147.87 | 23.82 | 10.01 | 7.8 | 4.07 | 4.35 |
August | 239.24 | 28.33 | 10.92 | 16.41 | 8.82 | 6.77 |
September | 65.79 | 8.76 | 6.53 | 4.48 | 4.39 | 3.54 |
October | 294.74 | 64.67 | 11.72 | 14.89 | 14.01 | 8.36 |
November | 58.17 | 9 | 4.51 | 5.86 | 2.97 | 3.06 |
December | 73.55 | 9.2 | 6.76 | 8.27 | 5.85 | 5.43 |
Area/ Off–High–Flat | Tianmu Lake Area | South Area | North Area | West Area | East Area | Central Area |
---|---|---|---|---|---|---|
2018 off season | 190.24 | 26.34 | 10.4 | 11 | 8.44 | 6.6 |
2018 flat season | 486.25 | 71.35 | 16.94 | 19.36 | 16.86 | 15.67 |
2018 high season | 703.01 | 115.11 | 19.54 | 23.4 | 16.97 | 14.33 |
2019 low season | 209.78 | 27.21 | 14.79 | 18.38 | 12.14 | 12.52 |
2019 flat season | 544.12 | 75.24 | 28.26 | 33.35 | 27.54 | 30.02 |
2019 high season | 798.04 | 129.5 | 38.78 | 45.63 | 31.93 | 25.91 |
Area Names | Industry Type | Industry Positioning | Trends |
---|---|---|---|
North Area | Secondary industry Tertiary industry | Mountain scenery Air travel | Down |
South Area | Tertiary industry Primary industry | Landscape tourism Eco-tourism agriculture | Up |
West Area | Secondary industry Tertiary industry | Food processing Metal building materials | Down |
East Area | Primary industry Tertiary industry | Efficient aquaculture Wetland tourism area | Down |
Tianmu Lake Area | Primary industry Tertiary industry | City card Tourism industry derivation | Up |
Central Area | Tertiary industry | Urban modern service industry Tourism distribution center | Down |
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Wu, H.; Chen, Z.; Yan, J.; Tang, X. Multi-Source Spatio-Temporal Data-Based Tourism Structure Analysis of Demonstration City for Global Tourism: Case Study of Liyang, China. ISPRS Int. J. Geo-Inf. 2022, 11, 547. https://doi.org/10.3390/ijgi11110547
Wu H, Chen Z, Yan J, Tang X. Multi-Source Spatio-Temporal Data-Based Tourism Structure Analysis of Demonstration City for Global Tourism: Case Study of Liyang, China. ISPRS International Journal of Geo-Information. 2022; 11(11):547. https://doi.org/10.3390/ijgi11110547
Chicago/Turabian StyleWu, Haoqi, Zhenan Chen, Jun Yan, and Xiaolan Tang. 2022. "Multi-Source Spatio-Temporal Data-Based Tourism Structure Analysis of Demonstration City for Global Tourism: Case Study of Liyang, China" ISPRS International Journal of Geo-Information 11, no. 11: 547. https://doi.org/10.3390/ijgi11110547