Journal of Resources and Ecology ›› 2021, Vol. 12 ›› Issue (5): 682-692.DOI: 10.5814/j.issn.1674-764x.2021.05.011
• Ecotourism • Previous Articles Next Articles
FENG Ling, GUO Jiaxin*(), LIU Yi
Received:
2020-09-06
Accepted:
2021-01-05
Online:
2021-09-30
Published:
2021-11-30
Contact:
GUO Jiaxin
Supported by:
FENG Ling, GUO Jiaxin, LIU Yi. Research Methodology for Tourism Destination Resilience and Analysis of Its Spatiotemporal Dynamics in the Post-epidemic Period[J]. Journal of Resources and Ecology, 2021, 12(5): 682-692.
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Models | The main content | Source |
---|---|---|
Destination Sustainability Framework (DSF) | Pointed out six elements of system resilience: Shock and stress; vulnerability; feedback loops; place; space; time | |
Scale, Change and Resilience Model (SCRM) | Emphasized the different ways community members respond to chronic and sudden stress | |
Community Disasters Resilience Framework (CDRF) | Pointed out that the community capital related to the resilience of the community after disaster mainly includes social capital, economic capital, physical capital, human capital, and natural capital | |
Sphere of Tourism Resilience (STR) | Proposed the key elements of a resilient tourism system: harness market forces, stakeholder cohesion, leadership, flexibility, adaptability, and learning | |
Tourism Disaster Vulnerability Framework (TDVF) | Pointed out that public and private sector institutions, individuals, communities, infrastructure, and natural environment are all factors affecting the vulnerability of tourism destinations |
Table 1 Representative tourism resilience models
Models | The main content | Source |
---|---|---|
Destination Sustainability Framework (DSF) | Pointed out six elements of system resilience: Shock and stress; vulnerability; feedback loops; place; space; time | |
Scale, Change and Resilience Model (SCRM) | Emphasized the different ways community members respond to chronic and sudden stress | |
Community Disasters Resilience Framework (CDRF) | Pointed out that the community capital related to the resilience of the community after disaster mainly includes social capital, economic capital, physical capital, human capital, and natural capital | |
Sphere of Tourism Resilience (STR) | Proposed the key elements of a resilient tourism system: harness market forces, stakeholder cohesion, leadership, flexibility, adaptability, and learning | |
Tourism Disaster Vulnerability Framework (TDVF) | Pointed out that public and private sector institutions, individuals, communities, infrastructure, and natural environment are all factors affecting the vulnerability of tourism destinations |
Dimensions | Primary indicators | Secondary indicators |
---|---|---|
Internal conditions of the tourism destination | Tourism economic system | Tourist arrivals, tourism revenue, tourism employment, the concentration of tourist market, etc. |
Tourism social system | Ratio of tourists to local residents, the structure of tourism talents, medical investment, internet penetration rate, etc. | |
Tourism institution and cultural system | Tourism warning system, tourism policy publicity system, tourism organization cooperation system, etc. | |
Characteristics of the epidemic | Tourism infrastructure system | Abundance of tourism resources, the number of hotels, the density of traffic network, the number of medical institutions, etc. |
Severity of the epidemic | Duration of the epidemic, the number of infections, death rate of the epidemic, distance from the epidemic center, etc. | |
Response capacity of the tourism destination | Losses caused by the epidemic | Direct loss of tourism revenue, loss of tourist arrivals, tourism unemployment rate, business operation, tourists’ willingness to travel, etc. |
Prevention and warning ability for epidemic | The response speed of tourism destination to epidemic, the number of tourism warning information sources, the number of tourism security institutions, etc. | |
Adaptive capacity of the tourism destination | Ability to recover from epidemic | The number of tourism support policies in the post-epidemic period, tourism emergency financial support, etc. |
Potential for tourism development | Growth rate of tourism revenue, growth rate of tourist arrivals, growth rate of tourism investment, etc. |
Table 2 Evaluation system of tourism destination resilience in the post-epidemic period
Dimensions | Primary indicators | Secondary indicators |
---|---|---|
Internal conditions of the tourism destination | Tourism economic system | Tourist arrivals, tourism revenue, tourism employment, the concentration of tourist market, etc. |
Tourism social system | Ratio of tourists to local residents, the structure of tourism talents, medical investment, internet penetration rate, etc. | |
Tourism institution and cultural system | Tourism warning system, tourism policy publicity system, tourism organization cooperation system, etc. | |
Characteristics of the epidemic | Tourism infrastructure system | Abundance of tourism resources, the number of hotels, the density of traffic network, the number of medical institutions, etc. |
Severity of the epidemic | Duration of the epidemic, the number of infections, death rate of the epidemic, distance from the epidemic center, etc. | |
Response capacity of the tourism destination | Losses caused by the epidemic | Direct loss of tourism revenue, loss of tourist arrivals, tourism unemployment rate, business operation, tourists’ willingness to travel, etc. |
Prevention and warning ability for epidemic | The response speed of tourism destination to epidemic, the number of tourism warning information sources, the number of tourism security institutions, etc. | |
Adaptive capacity of the tourism destination | Ability to recover from epidemic | The number of tourism support policies in the post-epidemic period, tourism emergency financial support, etc. |
Potential for tourism development | Growth rate of tourism revenue, growth rate of tourist arrivals, growth rate of tourism investment, etc. |
Data type | Access methods | Research applications |
---|---|---|
Basic data | Relevant management agencies and organizations | Map data, statistics, population data, etc. |
Epidemic data | Relevant management agencies and organizations, WHO website | Number of infections, death rate, the duration of the epidemic, etc. |
Service facility data | Relevant management agencies and organizations, network POI data | Number of medical institutions, tourist hotels, tourism resources, etc. |
News and policy data | Web text analysis | Tourism early warning information, tourism development support policy, etc. |
Visitors/business data | Questionnaires, telephone interviews, industry associations | Number of tourism enterprises, the operation of enterprises, the willingness of tourists to travel, etc. |
Tourism traffic data | Network data, airlines, traffic-related administrative departments | Flight route data, high-speed train frequency data, etc. |
Table 3 Multi-source data of tourism destination resilience in the post-epidemic period
Data type | Access methods | Research applications |
---|---|---|
Basic data | Relevant management agencies and organizations | Map data, statistics, population data, etc. |
Epidemic data | Relevant management agencies and organizations, WHO website | Number of infections, death rate, the duration of the epidemic, etc. |
Service facility data | Relevant management agencies and organizations, network POI data | Number of medical institutions, tourist hotels, tourism resources, etc. |
News and policy data | Web text analysis | Tourism early warning information, tourism development support policy, etc. |
Visitors/business data | Questionnaires, telephone interviews, industry associations | Number of tourism enterprises, the operation of enterprises, the willingness of tourists to travel, etc. |
Tourism traffic data | Network data, airlines, traffic-related administrative departments | Flight route data, high-speed train frequency data, etc. |
Research content | Main method | Elaboration |
---|---|---|
The evaluation system of tourism destination resilience in the post-epidemic period | Qualitative study | Use the methods of literature review, field investigation, deduction, and expert consultation to analyze the concept, connotation, and characteristics of tourism destination resilience in the post-epidemic period, to identify and locate typical case sites, and to construct the theoretical model and index system |
Measurement of tourism destination resilience in the post-epidemic period | Mathematical modeling analysis | Combined with the evaluation index system, use the methods of analytic hierarchy process (AHP), fuzzy evaluation, and entropy to determine the index weights; and construct a mathematical model to calculate the tourism destination resilience in the post-epidemic period |
Spatiotemporal difference analysis of tourism destination resilience in the post-epidemic period | Geospatial analysis | Use GIS spatial analysis tools, spatial auto-correlation model, and spatial hot spot detection model to analyze the spatiotemporal distribution characteristics of tourism destination resilience |
Factors affecting the spatiotemporal differences of tourism destination resilience in the post-epidemic period | Mathematical statistical analysis Geospatial analysis | Use correlation analysis, principal component analysis, factor analysis, and geographic detector model to identify the key factors that influence the spatiotemporal differences of tourism destination resilience and explore the influencing mechanisms |
Adaptive management mechanisms of tourism destination responses to the epidemic | Qualitative study | Combinied with the results of practical research, use the methods of literature review, inductive summary, and expert consultation to put forward some adaptive management policies and suggestions for tourism destinations to deal with the epidemic and improve resilience |
Table 4 Research method system for tourism destination resilience in the post-epidemic period
Research content | Main method | Elaboration |
---|---|---|
The evaluation system of tourism destination resilience in the post-epidemic period | Qualitative study | Use the methods of literature review, field investigation, deduction, and expert consultation to analyze the concept, connotation, and characteristics of tourism destination resilience in the post-epidemic period, to identify and locate typical case sites, and to construct the theoretical model and index system |
Measurement of tourism destination resilience in the post-epidemic period | Mathematical modeling analysis | Combined with the evaluation index system, use the methods of analytic hierarchy process (AHP), fuzzy evaluation, and entropy to determine the index weights; and construct a mathematical model to calculate the tourism destination resilience in the post-epidemic period |
Spatiotemporal difference analysis of tourism destination resilience in the post-epidemic period | Geospatial analysis | Use GIS spatial analysis tools, spatial auto-correlation model, and spatial hot spot detection model to analyze the spatiotemporal distribution characteristics of tourism destination resilience |
Factors affecting the spatiotemporal differences of tourism destination resilience in the post-epidemic period | Mathematical statistical analysis Geospatial analysis | Use correlation analysis, principal component analysis, factor analysis, and geographic detector model to identify the key factors that influence the spatiotemporal differences of tourism destination resilience and explore the influencing mechanisms |
Adaptive management mechanisms of tourism destination responses to the epidemic | Qualitative study | Combinied with the results of practical research, use the methods of literature review, inductive summary, and expert consultation to put forward some adaptive management policies and suggestions for tourism destinations to deal with the epidemic and improve resilience |
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