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Journal of Resources and Ecology >

2021 , Vol. 12 >Issue 5: 682 - 692

DOI: https://doi.org/10.5814/j.issn.1674-764x.2021.05.011

Ecotourism

Research Methodology for Tourism Destination Resilience and Analysis of Its Spatiotemporal Dynamics in the Post-epidemic Period

  • FENG Ling ,
  • GUO Jiaxin , * ,
  • LIU Yi
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  • School of Tourism Sciences, Beijing International Studies University, Beijing 100024, China
*GUO Jiaxin, E-mail:

Received date: 2020-09-06

  Accepted date: 2021-01-05

  Online published: 2021-11-22

Supported by

The Major Project of Beijing Social Science Foundation(19ZDA11)

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Abstract

As the COVID-19 pandemic continues to spread, the global tourism industry is facing enormous challenges. There is an urgent need to explore an effective path for tourism to recover and revitalize. With the normalization of the epidemic, tourism destinations will pay more attention to the prevention, warning, and coping strategies of the epidemic, and this focus will also be evident in the study of tourism destination resilience in the post-epidemic period. Some studies on the epidemic and the resilience of tourism are currently underway, but few of them are integrated with research on the resilience of tourism destinations in the post-epidemic period, although no systematic research ideas or methods have been found. Based on resilience theory, this paper summarizes the general research ideas and develops an epidemic resilience model suitable for urban tourism destinations. The present study also proposes a set of research methods based on the index system to analyze the resilience and its spatiotemporal dynamic characteristics of tourism destinations in the post-epidemic period. The methodology can be divided into three stages: Firstly, construct the conceptual model and evaluation system for tourism destination resilience; Secondly, select case sites for empirical analysis, measure the resilience of tourism destinations, and analyze the characteristics of spatiotemporal differences and subsequent factors of influence; And finally, establish an adaptive management mechanism for tourism destinations to use in response to the epidemic and in guiding the formulation of post-epidemic recovery policies.

Key words： epidemic; tourism destination; resilience; index system; spatiotemporal dynamics

Cite this article

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 . DOI: 10.5814/j.issn.1674-764x.2021.05.011

1 Introduction

Major epidemics have occurred frequently since the beginning of the 21st century, and new epidemics such as SARS, H1N1, MERS, Ebola, and COVID-19 have emerged one after the other, seriously affecting the typical development of the economy and societal growth. Tourism is sensitive and vulnerable to epidemics due to personal mobility, spatial clustering, industrial integration, and environmental dependence (Li et al., 2010), and typically bears the brunt of the impact. For instance, the SARS outbreak in China (2003) led to a sharp decline in the economic growth of Chinese tourism. The number of inbound tourists and foreign exchange revenue from tourism decreased by 6.38% and 14.61% year-on-year, respectively. In 2015, the MERS epidemic broke out in South Korea, resulting in a decrease in 2.1 million foreign tourists and a loss of tourism revenue of approximately USD 2.6 billion (Joo et al., 2019). The sudden outbreak of COVID-19 spread worldwide in 2020, and several countries and regions introduced travel restrictions that had a severe impact on the development of world tourism (Yang at al., 2020). The World Tourism Organization (UNWTO) reported that the number of international tourists would decrease by 20%-30%, and the subsequent expenditures would be reduced by USD 30-50 billion in 2020, compared with 2019 (UNWTO, 2020). Hence, the epidemic has had a significant impact on the tourism industry and has become one of the major crises affecting development.
Tourism is an integral part of the global economy, with an intense relationship and a long chain of the industry. It plays a vital role in promoting industrial development, guiding consumption, and creating jobs. When an epidemic occurs, the tourism industry suffers severe losses, resulting in a series of ripple effects that have an impact on the development of related industries. This type of situation leads to significant unemployment and a lack of economic growth (Page et al., 2012). Hence, the recovery and growth of tourism in the post-epidemic period are crucial for stimulating and releasing consumption potential, reviving and revitalizing economic development, and finally, stabilizing and improving the social environment. Due to the COVID-19 pandemic, (at the time of this writing) borders are closed; entire air fleets are grounded; and hotels, restaurants, and tourist sights are shut down, making tourism and transportation the most severely affected among all sectors (Zenker and Kock, 2020). Helping the global tourism industry recover from the COVID-19 pandemic has become a priority for the UNWTO.
Urbanization and globalization have accelerated the rapid spread of the virus, while the emergence or re-emergence of an epidemic is one of the consequences of global tourism and mobility (Richter, 2003). As the world is facing a spreading epidemic, it is essential to consider how a tourism destination can respond to the crisis and challenges, maintain its developmental vitality, and achieve a rapid recovery from this epidemic. However, there is currently no mature theoretical model or method for studying the recovery of tourism in a post-epidemic period. In this context, resilience theory provides a new research idea and paradigm for guiding tourism destinations in adapting to the external pressures and changes. The concept of resilience originated from ecology and was mainly used to study the resilience of an ecosystem at an early stage. After being introduced into the social system by Agder (1999), resilience has become a focal point of research in many social disciplines, such as urban and regional management, disaster planning, evolutionary economic geography, and community development (Hudson, 2010; Magis, 2010; Evans, 2011; Ross and Carter, 2011). The introduction of the resilience theory in tourism research during the epidemic not only provides a new perspective for understanding the impact of the epidemic, but also emphasizes the comprehensive ability of tourism destinations to cope with the epidemic. This is conducive to the sustainable development of tourism.
Based on the resilience theory, this paper constructed a set of research methods to study the resilience and spatiotemporal dynamic changes of tourism destinations in the post-epidemic period using an indicator system, in order to explore the recovery and development of urban tourism destinations. The specific research system was developed from the data sources, method organizations, and research contents. This study could provide theoretical support and reference for empirical studies, as well as guidance on the recovery and policy making for urban tourism.

2 Literature review

2.1 Epidemic and tourism

As the development of tourism depends on a peaceful travel environment, it can be highly vulnerable to diseases and epidemics (Jonas et al., 2011). Back to 2001, tourism research related to epidemic situations emerged when Khan et al. studied the impact of the Asian contagion on the tourism industry in Singapore (Khan et al., 2001). Then in 2003, the SARS outbreak had an immense effect on the tourism industry in many countries and regions, and researchers began to pay attention to the emerging epidemic crisis in tourism. At present, the epidemics documented in the literature on tourism mainly include SARS (Henderson, 2004; Pine and McKercher, 2004; Siu and Wong, 2004), H1N1 (Lee et al., 2012; Solarin, 2015), MERS (Shi and Li, 2017; Joo et al., 2019), Avian flu (Kuo et al., 2009), Lyme disease (Donohoe et al., 2015), foot-and-mouth disease (Frisby, 2003) and Ebola (Cahyanto et al., 2016). Depending on the differences in research scope and objects, tourism and epidemic research can be divided into two types: Macro-level research and micro-level research.
Earlier studies primarily focused on the macroscopic impact of the epidemic on tourism, which measured the loss of the tourism destination caused by the epidemic through the fluctuations in tourist arrivals, tourism revenue and tourism employment, and then proposed management strategies for tourism destinations to use in coping with the epidemic. For example, Mason et al. (2005) examined the impact of SARS on the aviation and hotel industries from a global perspective. McKercher and Chon (2004) studied the impact of SARS on the tourism sector in Asian countries. Statistics showed that in China, Hong Kong, Singapore, and Vietnam, which were severely affected by SARS, up to 3 million tourism employees became unemployed, resulting in economic losses of more than USD 20 billion. Pine and McKercher (2004) analyzed the impact of SARS on the Hong Kong tourism industry based on tourist arrivals, air travel, and the average hotel occupancy rate. Their results showed that the major epidemic represented by SARS could have a significant impact on the different scales of the tourism market, and at the same time, affect the destination and source market and even put a halt to tourism in a large part of the region (Henderson and Ng, 2004). As research has moved forward, studies on the macro-impact of an epidemic on tourism are no longer limited to fundamental description analysis. More and more econometric models are being applied to related research. For example, following the MERS outbreak in 2015, Joo et al. (2019) and Shi and Li (2017) used the seasonal autoregressive integrated moving average models and autoregressive distributed lag model (ADLM), respectively, to predict and estimate the losses of inbound tourists and economics in relevant service sectors in South Korea. These estimates have again supported previous findings that the occurrence of a major epidemic could cause damage to the entire economic system of the affected destination.
The micro-level research mainly focused on the state and response of tourists and tourism companies in the context of the epidemic. Health risk is a vital factor affecting the choice of a tourism destination (Chien et al., 2017), as the outbreak of the epidemic undoubtedly raises the sensibility of tourists. In the travel decision-making process, the tourists’ perception of the risk may be more significant than the actual situation of the destination (Mizrachi and Fuchs, 2016). The change in the consumption psychology of tourists will have a further impact on their consumption behavior. The empirical study of Zhang et al. (2005) on Chinese tourists after the SARS outbreak showed that the epidemic had a significant effect on travel preferences, especially the travel mode and type. After the outbreak, tourists tended to opt for outdoor activities and eco-tourism, while urban residents preferred rural and suburban travel. However, the impact of an epidemic on the psychology and behavior of tourists will vary with the individual, and their subjective knowledge, age, gender, and other demographic characteristics will all have an impact (Cahyanto et al., 2016). The research on tourism businesses mainly focused on the effects of the epidemic and its countermeasures. The decline in demand, the decrease in profits, and the shortage of employees are common problems faced by tourism companies in an epidemic situation. Besides, different tourism enterprises are affected to varying degrees. Compared with small enterprises, large multinational enterprises are more severely impacted but more resilient (Gu and Wall, 2016). However, an epidemic will also bring opportunities for tourism business, as some enterprises and scenic spots may benefit from the new demand for tourism by taking advantage of changes in tourist consumption patterns, and then become an emerging industry (Zeng et al., 2005; Gu and Wall, 2016). Reducing costs, actively exploring new markets, establishing effective communication, and maintaining staff morale are all effective measures for tourism enterprises to deal with an epidemic in the face of these opportunities and challenges (Lo et al., 2006).

2.2 Resilience and tourism

The word resilience originated from the Latin word “resilere”, translated as “to spring back”, and therefore implies a certain degree of flexibility (Filimonau and De Coteau, 2020). The concept of resilience was first proposed by Holling in the field of ecology, which refered to “the time required for an ecosystem to return to equilibrium or steady- state following a perturbation” (Holling, 1973). According to the resilience theory, the ecosystem not only existed in a stable state but also had a multi-stable structure. When the function and basic structure of the system crossed a threshold, the system would change from one stable state to another. Once the ecosystem had crossed the threshold, it would be difficult or impossible to return it to its previous state. This theory emphasizes the variability and adaptability of the system and has gradually been applied to other fields, such as sociology, psychology, management, economics, etc. (Agder, 1999; Aldunce et al., 2014). Although there are differences in the connotation of resilience in different fields, one common element is that resilience is understood as the ability of a system to cope with changes and disturbances (Manyena, 2006).
In tourism research, the concept of resilience has emerged as an addition and extension to the sustainable development paradigm, in order to improve the building capacity of the tourism destination system and help it to be restored to the ideal state after unexpected events (Basurto-Cedeño and Pennington-Gray, 2018). Over the past two decades, the frequency and intensity of man-made and natural disasters have increased, and researchers have developed a keen interest in understanding the vulnerability and resilience of tourism destinations and their driving factors (Van der Veeken et al., 2016). The existing research on tourism resilience has studied it from the aspects of protected area tourism (Strickland-Munro et al., 2010), tourism destination management (Gretzel and Scarpino-Johns, 2018; Filimonau and De Coteau, 2020), disaster recovery (Calgaro and Lloyd, 2008; Bhati et al., 2016), community tourism planning (Chen et al., 2020), the vulnerability of industries (Sheppard and Williams, 2016) and others. Among them, disaster recovery is a vital branch of tourism resilience research. A growing number of tourism destinations are beginning to explore disaster reduction and post-disaster improvement strategies.
The existing studies on the resilience of tourism after disasters present three characteristics. Firstly, from the perspective of disaster types, existing studies primarily focus on natural disasters, such as tsunamis, hurricanes, floods, etc. (Calgaro and Lloyd, 2008; Lamanna et al., 2012; Ghaderi et al., 2015), and pay less attention to the recovery of tourism after man-made disasters, political and economic crises, and epidemics. Secondly, from the perspective of research methods, existing approaches focus on the qualitative case analysis, using field surveys, semi-structured interviews, focus groups and other methods, and pay attention to the construction of tourism resilience models. Table 1 shows the representative models of tourism resilience in existing studies. Thirdly, from the research content perspective, the existing studies focus on exploring the driving factors that affect the post-disaster resilience of tourism destinations. The vulnerability and resilience of tourism destinations are composed of a variety of dynamic and interactive elements, including the geographical environment, development characteristics, social structure, governance, and others (Calgaro et al., 2014). Thus, the resilience of different tourism destinations is affected by various factors. Calgaro and Lloyd (2008) put forward 13 factors, such as natural terrain, development model, livelihood choice, economic capital, and government management, in a post-tsunami disaster study on the resilience of Khao Lak tourism. Van der Veeken et al. (2016) pointed out that financial capital, social capital, human capital, disaster cognition, tourism destination image, local governance, and marketing strategy are the common elements affecting the vulnerability of tourism destinations. Besides, it is also worth mentioning that many studies have proposed that stakeholder cooperation is the key to enhancing the resilience of destinations (Calgaro and Lloyd, 2008; Ghaderi et al., 2015; Filimonau and De Coteau, 2020).
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 Calgaro et al. (2014)
Scale, Change and
Resilience Model (SCRM)		 Emphasized the different ways community members respond to chronic and sudden stress Lew (2014)
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 Peacock et al. (2010)
Sphere of Tourism
Resilience (STR)		 Proposed the key elements of a resilient tourism system: harness market forces, stakeholder cohesion, leadership, flexibility, adaptability, and learning Cochrane (2010)
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 Becken et al. (2014)
There is no doubt that the outbreak of the epidemic will seriously affect the development of tourism. But at the same time, it should be recognized that it is normal for the tourism industry to be disrupted by external conditions. The tourism industry has strong resilience and can recover from various crises. However, existing studies mainly focused on the impact of an epidemic crisis on tourism, while few studies paid attention to the role of tourism resilience in coping with an epidemic crisis. We ignored the initiative of the tourism industry in previous studies. Now, resilience theory provides a new perspective to fill this research gap. However, the previous tourism resilience models are mainly aimed at natural disaster crises, so they are not suitable for the epidemic crisis because of its suddenness, transmission and comprehensiveness. Therefore, it is necessary to construct a suitable model to assess the resilience of tourism destinations in the post-epidemic period.

3 Research idea and model

3.1 General idea

The purpose of this paper is to construct a set of research methods for tourism destination resilience in the post-epidemic period using an index system which is based on the resilience theory and multi-source data. Firstly, on the basis of the previous literature and theories, this study constructs the theoretical framework and index system of tourism destination resilience in the post-epidemic period from multi-source data and analyzes its spatiotemporal characteristics. Secondly, it explores the key factors and mechanisms that affect the spatiotemporal differences in tourism destination resilience. Finally, the adaptive management countermeasures for tourism destinations to employ in response to future epidemics are proposed.

3.2 Theoretical support

According to the general idea of tourism resilience research, the various steps are to define the research system, analyze the research system, evaluate system resilience, and simulate the future scenarios. This study constructs an analytical framework based on the multiple disciplines of vulnerability theory, resilience theory, and adaptive management theory.
Identifying and reducing the crisis and vulnerability of the system is the key to the existing resilience models (Tierney and Bruneau, 2007). The first step of this study is to identify the vulnerability factors for tourism destinations in an epidemic based on vulnerability theory. The development of tourism activities relies on the social and ecological resources of a tourism destination. Tourists, tourism activities, and the socio-ecological environment of tourism destinations together form a complex tourism system. Vulnerability is one of the inherent characteristics of that system and is the reason why tourism is affected by external events such as epidemics. However, it should be recognized that tourism destinations have a strong resilience, and can recover from various crises, which are consistent with the variability and adaptability of the resilience concept. The application of resilience theory can help us to better understand the state and change of tourism destinations in the face of an epidemic. The ultimate goals of the discussion about the characteristics, measurement, and influencing factors of the tourism destination resilience are to create an adaptive management mechanism, enhance the learning capacity and adaptability of stakeholders in the system, and promote the sustainable development of tourism destinations in a future crisis. Therefore, based on adaptive management theory, practical strategies for tourism destinations to cope with an epidemic and reduce their vulnerability should be proposed in the final part of the study, thus forming effective closed- loop research, as presented in Fig. 1.
Fig. 1 Theoretical basis for research on tourism destination resilience in the post-epidemic period

3.3 Conceptual model

Theoretical models and frameworks are basic abstractions of actual events and phenomena. By refining the important characteristics of events and phenomena, as well as the interrelations between internal structures, we can enhance our understanding of them. In 2008, Cutter et al. (2008) proposed the Disaster Resilience of Place (DROP) model. In this model, the antecedent conditions, the characteristics of the hazard event, and the coping responses were combined with having an impact on the system. Among these components, the internal antecedent conditions determined the inherent vulnerability and resilience of the system; the characteristics of hazard events were defined by duration, intensity, and frequency; and coping responses included learning and the ability to absorb. The DROP model has two significant characteristics. First, it emphasizes the suddenness of a natural disaster crisis and is also applicable to other types of sudden natural crisis, which is consistent with the major public epidemic crisis studied in this paper. Second, the model focuses on the resilience of small-scale regions, pays attention to the driving role of local factors in the post-crisis recovery stage, and recognizes the regional differences of resilience to a certain extent, which provides a reference for the temporal and spatial analysis of tourism destination resilience in this study. Based on the DROP model, this paper develops an epidemic resilience model suitable for small-scale tourism destinations, as shown in Fig. 2.
Fig. 2 Epidemic resilience of tourism destinations
Tourism destination resilience in the post-epidemic period refers to the ability of a tourism destination to carry out a series of recovery and adaptation measures based on its own development conditions to reduce the damage to the destination tourism system caused by an epidemic crisis. As shown in the model, the resilience of a tourism destination to an epidemic depends on four aspects: the internal conditions, characteristics of the epidemic, the response capacity, and the adaptive capacity of the tourism destination. These four aspects are represented in the model with a “+” or “±” to connect them, where “+” indicates that it can increase the pressure on the tourism destination, and “±” means that it works in both amplified and attenuated directions. As a disturbance factor, the epidemic has an impact on the development of the tourism destination system. When the epidemic occurs, the subsystems of the tourism destination system are facing challenges, including the tourism economic system, tourism social system, tourism institution and culture system, and tourism infrastructure system. These elements are the preconditions for the development of tourism destinations in the non-epidemic period and reflect the growth potential. When a tourism destination system integrates various resources during the epidemic period to deal with the crisis, these elements determine the vulnerability and resilience of that tourism destination. The characteristics of the epidemic are the external conditions that affect the vulnerability of tourism destinations, including the duration and intensity of the epidemic, such as the number of infections in the tourism destination, the distance from the epidemic center, etc. The internal conditions of the tourism destination interact with the characteristics of the epidemic, which together determine the vulnerability of the tourism destination during the pandemic. Therefore, it is represented in the model with a plus sign.
However, high vulnerability does not imply low resilience. Huge economic benefits for tourism and comprehensive policies will improve the coping capacity of the tourism destination system. Under pressure, the response and adaptive capacity of a tourism destination can help it to cope with the disturbance caused by the epidemic and either recover to the original state as soon as possible or form a new state. Among them, the response capacity reflects the steps taken by tourism destinations to cope with the epidemic, such as prediction and warning, exchange and cooperation, risk control, etc., emphasizing the short-term response- ability of the tourism destination under the epidemic pressure. Adaptive capacity reflects the ability of tourism destinations to maintain long-term sustainable development under the epidemic conditions, mainly emphasizing the potential for the development of a destination. In addition, the response and adaptive capacity, changing dynamically, works both ways in affecting the resilience of the tourism destination. With the spread of the epidemic, the tourism destination will take different response measures, which can change its response and adaptive capacity accordingly to weaken or enhance its resilience in the post-epidemic period.
The above four dimensions together determine the resilience of the tourism destination in the post-epidemic period. When the resilience of the tourism destination is weak, the original tourism system will be affected, and the development of the tourism destination will be temporarily paralyzed. At the same time, the impact and harm of the epidemic on tourism destinations will be further intensified, thereby highlighting the vulnerability of the destination. Then, the tourism destination is likely to fall into a vicious circle of development, and once a certain threshold is exceeded, it will completely change into the next cycle. If the resilience is strong, the tourism destination can restore and adjust itself, preserve the stability of the original system, and mitigate the epidemic crisis. Meanwhile, it can learn and incorporate the experience into the repair process, further improve internal conditions and reduce vulnerability factors to improve its capacity for sustainable development, and finally form a virtuous circle of growth.

4 Research framework

Based on the previous research and conceptual model, the next step is to develop a research framework that follows a three-pronged approach: tourism destination resilience evaluation model - an empirical study on tourism destination resilience - adaptive management mechanism of tourism destination. This framework combines a theoretical level, a practical level, and a guaranteed level. Then, the research system will be summarized from three aspects: research contents, data sources, and methods.

4.1 Research contents

4.1.1 The evaluation system for tourism destination resilience in the post-epidemic period
Most of the existing studies on tourism resilience use quantitative analysis methods, which makes it difficult to compare and analyze the resilience of different tourism destinations. Quantitative measurement of resilience has always been a difficult issue, but several related studies exist. For example, Hyman (2014) built vulnerability indicators for tourism communities based on the environmental, technical, economic, social and institutional factors, and assessed the vulnerability of Jamaican coastal and non-coastal tourism communities to climate change. As an evaluation tool, the comprehensive index system can help us to better evaluate some complicated concepts and provide methodological guidance for the resilience measurement and spatiotemporal analysis of tourism destinations (Blancas et al., 2010).
First, the study should combine the above-mentioned conceptual model in order to construct the evaluation index system of the tourism destination resilience in the post-epidemic period theoretically. The evaluation index system should be able to reflect the corresponding theoretical basis comprehensively and systematically. In addition, the availability and consistency of data should be considered and follow the principles of scientific validity, representativeness, and operability. It is proposed to construct the index system from four dimensions, which are the internal conditions, the characteristics of the epidemic, the response capacity, and the adaptive capacity of the tourism destination, as presented in Table 2.
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.
In the above indicator system, the internal conditions of the tourism destination essentially reflect the vulnerability and exposure of the tourism system under the epidemic crisis, mainly related to economic, environmental, infrastructure and other relevant factors (Fuchs et al., 2011). Its indicators represent the background conditions of the destination and the changes caused by tourism development. The characteristics of the epidemic mainly reflect the degree of epidemic development, which will affect tourists’ perception of destination security, cause tourists to change travel decisions, and then affect the recovery of the tourism destination in the post-epidemic period (Timothy and Robert, 2008). Its indicators are selected from two aspects, severity of the epidemic and losses caused by the epidemic. The response and adaptive capacity of the tourism destination refer to the human, material, financial and management capacity that the tourism system can acquire when resisting the influences of adverse environmental factors. Therefore, the selected indicators mainly reflect the efforts of the tourism destination to deal with the epidemic.
4.1.2 Measurement of tourism destination resilience in the post-epidemic period
Based on the index system, the resilience of the tourism destination is measured empirically by selecting cases and collecting relevant data. Tourism resilience in this study refers to the ability of the tourism destination to recover during the post-epidemic period by taking a series of recovery and adaptation measures to reduce the damage caused by the epidemic. According to this definition, the smaller the loss of the tourism destination in the epidemic state is, the stronger the recovery capacity will be, and vice-versa. Referring to previous studies (Tian and Fang, 2019), the following methods are used to calculate the resilience of the tourism destination:
$R=\frac{1}{I-Q}\times 100$
$Q={{F}_{\text{res}}}/{{F}_{\text{los}}}$
where R represents the resilience of the tourism destination in the post-epidemic period; and I is the development capacity of the tourism destination while it is not affected by the epidemic; Fres represents the ability of a tourism destination to recover by adapting and responding to the epidemic; Flos represents the losses of the tourism destination from an epidemic.
The equation for the calculation of I is:
$I=TES\times {{W}_{TES}}+TSS\times {{W}_{TSS}}+TICS\times {{W}_{TICS}}+TIS\times {{W}_{TIS}}$
where TES, TSS, TICS, and TIS represent the tourism economic system, social system, institution and culture system, and infrastructure system, respectively, and W is the respective weight of each.
Q represents the tourism development capacity of the tourism destination under the epidemic, calculated by Fres and Flos.
Fres represents the ability of a tourism destination to recover by adapting and responding to the epidemic. The corresponding equation becomes:
${{F}_{\text{res}}}=ICD\times {{W}_{ICD}}+RCD\times {{W}_{RCD}}+ACD\times {{W}_{ACD}}\text{ }\!\!~\!\!\text{ }$
where ICD, RCD, and ACD represent the internal conditions, response capacity and adaptive capacity of the tourism destination, respectively, and W is the respective weight of each;
Flos represents the losses of the tourism destination from an epidemic. The equation for its calculation is:
${{F}_{\text{los}}}=SOE\times {{W}_{SOE}}+LOE\times {{W}_{LOE}}$
where SOE and LOE represent the severity of the epidemic and the losses caused by the epidemic, respectively, and W is their respective weights.
The subtraction of Q from I indicates the losses of the tourism destination development ability under the epidemic.
4.1.3 Spatiotemporal difference analysis of tourism destination resilience in the post-epidemic period
From the above index system, it is clear that the resilience of a tourism destination during the post-epidemic period is influenced by several factors, so the resilience of different destinations has to be different obviously. It is necessary to analyze their spatiotemporal dynamic characteristics. This part of the study can be carried out from two dimensions: 1) In the temporal dimension, the latest outbreak of the COVID-19 pandemic shows that an epidemic has a wide range of impact and long duration. The tourism destination may be in an epidemic state for a long time. Therefore, it is necessary to obtain data on tourism destinations for many years in the post-epidemic period, to analyze the change of tourism destination resilience over time, and to explore the recovery cycle of the tourism industry. 2) In the spatial dimension, data are required for multiple tourism destinations in the same period and their resilience levels must be calculated separately. Then GIS spatial analysis theories and tools can be used to analyze the distribution of tourism destination resilience horizontally in the spatial dimension and to study the degrees of spatial aggregation and correlation.
4.1.4 Factors affecting the spatiotemporal difference of tourism destination resilience in the post-epidemic period
After analyzing the spatiotemporal differences of the tourism destination resilience, there is still a need to examine the reasons behind the differences, identify the key factors influencing the spatiotemporal change of the resilience, and summarize their mechanisms to provide theoretical support for improving the internal development of tourism destinations. This part of the study should focus on three main issues: 1) To what extent the factors explain the spatiotemporal variability of tourism destination resilience; 2) Whether the interactions of different factors increase or weaken the explanation of the spatiotemporal differences in the tourism destination resilience during the post-epidemic period; and 3) Whether there are significant differences in the influences of different factors on the spatiotemporal differences of tourism destination resilience during the post- epidemic period.
4.1.5 Adaptive management mechanism of tourism destination response to the epidemic
The ultimate goal of this study is to ensure that theoretical research serves practical policy. After identifying the factors influencing tourism destination resilience, we need to combine the actual situation of the epidemic in the tourism destination and the measures used to deal with previous outbreaks in order to build an adaptive management mechanism that allows the tourism destination to cope with the epidemic. Based on the adaptive management theory, the future of the tourism destination resilience to an epidemic can be improved in the following aspects: combining the internal development conditions of the tourism destination, enhancing the capacity of the tourism destination to cope with the epidemic, improving the potential for tourism development during the epidemic, and strengthening the prevention and warning systems of tourism destinations.
The recovery and development of the tourism destination in the post-epidemic period is an important issue for the industry. Research on tourism destination resilience and spatiotemporal differences in the post-epidemic period can not only theoretically enrich the perspective of tourism and epidemic research, but it can also provide practical guidance for decision-making on the formulation of differentiated and precise recovery policies and enhance the crisis-coping capacity of the tourism destination. The five key research issues mentioned above are interrelated and progressive. The research framework includes the theoretical level, the practical level, and the guaranteed level, as shown in Fig. 3. The theoretical level provides the theoretical support for the research; The practical level offers the practical basis for the strategy; The guaranteed level provides the policy guidance, and the latter two, in effect, continue to enrich and improve the theoretical level.
Fig. 3 Main contents of tourism destination resilience study in the post-epidemic period

4.2 Data sources

Existing literature indicates that reliance on a single data channel is a common problem in the study of tourism and epidemics or the study of tourism resilience. These data are mainly collected through the relevant management agencies and organizations, questionnaire surveys, second-hand information, and other networks. The timeliness and authenticity of such data are inadequate, thus affecting the accuracy and scientific findings of the research. Data on tourism development and epidemics are now more readily accessible due to the rapid growth of information and communication technologies and the creation of relevant data platforms. In combination with the index system mentioned above, the research can incorporate multi-source data by combining traditional data with big data to improve the data quality and accuracy, as shown in Table 3.
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.

4.3 Research methods

At present, the study of tourism and epidemics mainly focuses on quantitative analysis, while the study of tourism resilience primarily focuses on qualitative analysis. Because of the complexity of the situation, the present study needs to set up a research method system which combines qualitative and quantitative analysis based on the research scale and specific problems, so that it may give full weight to the complementary structure and mutual benefit of the two research methods, as depicted in Table 4.
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

5 Discussion

With the normalization of the epidemic, tourism destinations should pay more attention to the prevention, warning, and coping strategies of the epidemic, and the study of the resilience of tourism destinations in the post-period of the epidemic will also receive attention. In this paper, we have tried to develop a set of research methods for analyzing tourism destination resilience during the post-epidemic period. In the practical case study, the integration of multi-source data and multi-disciplinary analytical methods still need to be strengthened. In addition to the research issues mentioned above, future research on tourism destination resilience in the post-epidemic period can be expanded in several aspects. 1) We can continue to carry out dynamic monitoring and assessment of tourism destination development in the post-epidemic period, especially in the period following the implementation of revitalization measures. Assessment is required after the implementation of a new policy, as well as the impact on the resilience of the tourism destinations. 2) The scale is an essential element for resilence studies (Calgaro and Lloyd, 2008). This research system is more suitable for small-scale studies, such as comparative research among different urban tourism destinations. However, a variety of research methods and policies should be explored for larger-scale tourism destinations, both nationally and globally. 3) When resilience is either strong or weak beyond a certain threshold following an epidemic, the tourism destination will enter a new stage of the developmental cycle. However, determining the threshold is difficult to judge for now. In the future, the application of mathematical statistics and mathematical modeling should be strengthened by using multi-source data, and scenario simulation can be uesd to study and predict the resilience thresholds of the tourism destinations in the post-epidemic period. Furthermore, in addition to the local factors mentioned above, some qualitative destination characteristics also have an impact on tourism destination resilience in the post-epidemic period. For instance, the resilience varies among different types of tourism destinations. Rural and scenic tourism destinations may recover more easily than urban and thematic tourism destinations in the post-epidemic period. The resilience also varies by region, and coastal destinations are more vulnerable to the epidemic than those in inland areas. Therefore, in the future, we can also study the resilience of different tourism destinations according to the classification of their types and specific characteristics.

6 Conclusions

Currently, there are several studies on the epidemic effects and resilience of tourism in general, but only a few of them are integrated for studying the resilience of post-epidemic tourism destinations, and no systematic research ideas or methods have been reported so far. Especially with the continuing spread of the COVID-19 pandemic, the tourism destination faces immense challenges and there is an urgent need to explore effective ways of rejuvenation. In order to focus on the post-epidemic theme of tourism destination resilience, this paper proposed a set of methods for resilience measurement and spatiotemporal dynamic analysis based on an index system, multi-source data, and qualitative and quantitative methods. The research is divided into three stages: Firstly, construct the conceptual model and evaluation system for the resilience of the tourism destination in the post-epidemic period; Secondly, select case sites for empirical analysis, evaluate the resilience of tourism destinations, analyze the characteristics of spatiotemporal differences and influencing factors; And finally, construct an adaptive management mechanism for tourism destinations to deal with the epidemic, and guide the formulation of post-epidemic recovery policy.

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