Tourism is an underlying industry that promotes the development of the global economy [1
]. According to the World Travel & Tourism Council (WTTC), tourism contributed 10.3% (8.9 trillion US dollars) of global GDP and provided one-tenth of the total number of jobs (330 million positions) in 2019 before the pandemic [2
]. Through developing the tourism industry, local governments can markedly improve the level of infrastructure construction, increase employment opportunities, improve people’s living conditions, and promote urban economic growth [3
]. In addition, tourism development is a fundamental part of a sustainable development strategy, which is recognized as a green industry by the world due to its low energy consumption and light pollution characteristics in the development process [6
Despite being one essential force promoting regional economy, regional tourism itself is greatly influenced by socioeconomic status [7
], including GDP [8
], employment status [9
], personal income [10
], health and hygiene [7
], industrial production index [11
] and social media [12
]. Besides the socioeconomic condition, research also identified the notable role of the environment in affecting regional tourism [13
], especially climatic conditions, such as temperature [16
], precipitation [17
], sunshine [18
], and relative humidity [19
]. Road infrastructure was also a critical environmental driver enhancing the tourism industry [20
]. However, all these previous studies only adopted a limited number of factors. It is necessary to consider the comprehensive impacts on tourism by combining socioeconomic conditions with environmental conditions.
When investigating relationships between regional tourism and potential explanatory factors, an unrealistic assumption persistently embedded in previous literature was that the variables’ relationships were homogeneous, which had been defined as stationarity. For instance, non-spatial tourism studies using qualitative analysis [22
], feasible generalized least square (FGLS) regression [19
], linear and quantile regression [23
], or logit regression [24
] are regarded as global-scale analyses and also ignore the existence of spatial effects. Likewise, some geospatial tourism studies using the spatial regression models, such as the exploratory spatial data analysis (ESDA) [25
] or spatial econometric models [26
], are capable of incorporating spatial effects for intercept or residual but are still unable to estimate a set of space-scale coefficients to characterize the varying region-specific relationships between variables. Hence, a more reasonable assumption in the real world highlights the heterogeneous or varying impacts of explanatory variables on tourism development due to region-specific situations, especially for studies conducted across large domains at finer geospatial scales. Such spatially heterogeneous variables relationships are called spatial nonstationarity in the field of statistics. At present, the geographically weighted regression (GWR) [27
] is frequently used in tourism research, aiming at exploring such spatial nonstationarity between tourism and various influencing factors [28
]. However, to the best of our knowledge, no study has been conducted from the spatiotemporal integrated nonstationary perspective, to fully explore both socioeconomic and environmental drivers on regional tourism development.
In China, as the area of interest in this study, there has long been an issue of regional tourism development disparities [30
], which obstructed regional tourism sustainability to some extent [31
]. Although these geospatial disparities have been extensively discussed at a provincial-level scale [32
] or city group scale [33
], seldom have studies explored the city-specific disparities of regional tourism, especially over mainland China. Based on tourism connotations and tourism elements, Chinese scholars have established a comprehensive indicator framework of influencing the urban tourism industry from multiple dimensions. Socioeconomic and environmental aspects are also considered indispensable indicators reflective of regional tourism industry development [34
]. However, no existing studies ever investigated the joint impacts of socioeconomic and environmental conditions on China’s city-level tourism from a spatiotemporal heterogeneous perspective, to provide evidence-based implications for assisting the formulation of tourism-related policies at governmental levels in a timely and effective manner.
In an attempt to find effective factors affecting regional tourism outcomes to provide tourism strategies tailored for specific local spatial conditions and changing temporal circumstances, we constructed an explanatory variable framework composed of 30 variables, including socioeconomic and environmental conditions. We explored spatiotemporal heterogeneous relationships between the regional tourism development and the multi-source explanatory factors from 2008 to 2017 across Chinese cities by employing the Bayesian spatiotemporally varying coefficients (STVC) model [35
]. The establishment of such an explanatory variable framework in our study also served as a contributor to the current literature in this field in terms of improving the comprehensiveness of the existing research index system as well as adding novel perspectives into this field based on the consideration of both spatial and temporal heterogeneity.
In this study, the multidimensional impacts of socioeconomic and environmental variables, including linear and nonlinear numerical effects and spatiotemporal heterogeneous effects, on regional tourism were comprehensively investigated across Chinese cities along with the first production of a set of spatiotemporal maps depicting China’s total tourism revenue. These findings may add innovative insights about the mechanisms of how multi-source geospatial factors have affected the regional tourism industry, and is expected to provide a brand-new viewpoint for policymakers. According to different scales, we have some conclusions, as follows.
Globally, significant effects of both socioeconomic and environmental variables were identified [28
], which highlighted the necessity of taking a wide range of factors into accounts throughout the procedure of tourism policies formulation. Tourism is a comprehensive industry composed of multiple elements, including food, shelter, transportation, travel, entertainment, and purchase. However, the importance of some of these elements embedded in the tourism industry, such as food, shelter, and transportation, is always ignored for the reason that they are simply regarded as the basic service facilities of a city. Therefore, the positive effects of the socioeconomic and environmental factors on tourism are supposed to be focused on the industrial level, which suggests that the idea of developing industries should always be adopted as the guideline for developing the tourism industry regardless of regional or national levels. At present, the “Travel +” strategy being implemented by the Chinese government is exactly based on this idea [58
Temporally, the development of China’s tourism has mainly benefited from comprehensive time–scale impacts of multiple factors. Based on temporal nonstationarity, the predominant stimulants for tourism development were demonstrated to have gradually switched from the regional economy, populational size, and tourism resource attractiveness to personal economic status. These results implied that China’s current tourism industry demonstrated a new feature that a transition from sightseeing tourism to leisure and holiday tourism is very much likely to occur. Meanwhile, residents’ affluence has been highlighted as an indispensable contributor to nationwide tourism development [59
]. Under such a changing background of the tourism industry in China, it is highly suggested that improving personal income, as well as safeguarding the rights and interests of employees, should be adopted as an essential strategy for facilitating the nationwide tourism industry development, which might be achieved via the implementation of multiple tourism-related policies at governmental levels, such as approving paid-leave policies for employees, encouraging enhanced flexibilities of work schedules to be tailored for vocational leaves, as well as encouraging off-peak vocational arrangements.
Spatially, the development of China’s tourism could be characterized as “strong in the east and weak in the west” [30
], which was affected by various factors. Cities of West China were mainly affected by population size and tourism resources, while personal income, employment and urbanization had more contributions to cities in the east region [60
]. The city-level spatial nonstationarity found in this study could serve as an acceptable reference in the procedure of making more targeted policies by governments at all levels. For example, the western region may put forward corresponding talent introduction policies while promoting economic development. In addition, the local government can develop sightseeing and holiday tourism through developing natural landscapes. Cities of East China need to focus on optimizing the protection system of workers’ rights and interests and developing characteristic tourism products to provide tourists with high-end, comfortable, and personalized services for stimulating tourism. Northeast China may focus on infrastructure and strengthen the planning and laying of the road networks to enhance regional tourism accessibility. Furthermore, city-level local authorities could utilize local resources rationally and determine the direction of tourism strategies by using the critical drivers’ local spatial influencing maps to support ecotourism, sightseeing tourism, vacation tourism, geological tourism, and urban tourism. In addition, the first series of maps displaying China’s tourism revenue’s spatiotemporal distributions at an administrative city level from 2008 to 2017 was produced, which was further analyzed to provide urbanization-related insights into empirically optimizing the unbalanced development of the tourism industry [61
To sum up, from the multidimensional spatiotemporal heterogeneous perspective, the government should formulate various tourism policies based on region-specific conditions, as well as pursue the development concept of “applying proper measurements in line with local conditions and temporal variations”. At present, tourism industry development in areas with relatively high urbanization levels has demonstrated a change from sightseeing tourism to leisure tourism. As a result, socioeconomic status should be continuously considered as a significant factor throughout tourism-related policy-making procedures in these regions. In contrast, regarding cities with low-level urbanization distributed in West China, environmental factors or sightseeing resources, instead of other factors, should be addressed as predominant issues to be considered throughout the formulation of tourism-related policies [60
]. Therefore, making city-specific strategies that take city-specific factors into account is expected to improve the accuracy of policy formulation, as well as the effectiveness of strategic implementation, which would further mitigate both “invalid policy” and “weak policy” produced by the “one-size-fits-all” policy.
Finally, we would like to underline the importance of the local spatiotemporal regression approach, namely, the Bayesian STVC model we have selected. As discussed above, introducing a spatiotemporal heterogeneous perspective to regional tourism management could avoid the one-size-fits-all issue via providing multidimensional spatiotemporal information. In the spatial statistics field, local regressions that can deal with such spatiotemporal heterogeneity among variables relationships (spatiotemporal nonstationarity) are relatively rare, which can be generally classified into the frequentist-type model [63
] and the Bayesian-type model [35
], as they were proposed independently under different statistical traditions. The main reasons we chose the Bayesian STVC model as the applied local spatiotemporal regression lie in the following considerations. First, only the Bayesian-based local spatial or spatiotemporal model is an actual “full-map” modeling technique; thus, the results are more reliable [67
]. Second, the Bayesian STVC model follows a space–time independent nonstationary assumption, dramatically reducing the computational burden and weakening the overfitting problem. Last but not least, due to its separately fitting of space-coefficients (SCs) and time-coefficients (TCs), the Bayesian STVC model is more user-friendly: stakeholders can directly separately obtain the spatial and temporal autocorrelated regularities [36
]. Beyond these benefits, the Bayesian STVC model still needs further improvement to solve more complex space–time interaction issues in natural and social sciences.
This study verifies that socioeconomic and environmental factors simultaneously affect tourism development over China, globally and locally, supported by the up-to-date space-time data of city-level tourism statistics and a series of advanced Bayesian regressions. Remarkably, the local impacts of socioeconomic and environmental conditions vary heterogeneously at the city level in both time and space dimensions across China, and was demonstrated by adopting the cutting-edge Bayesian STVC model, which was also used for estimating the first series of spatiotemporal maps of city-level tourism development. These fruitful findings provide novel insights into policy-making procedures at multiple levels. Here, the Bayesian STVC model was successfully applied to mine the spatial and temporal autocorrelated nonstationarity inherent in tourism–covariates relationships over China and could serve as an emerging tool to offer new insights on spatiotemporal-oriented influencing factor analysis and high-precision prediction in broader GIScience-related fields of social and natural sciences.
Apart from all these achievements, several concerns should be better addressed in future lines of research. First, the seasonal effect is the main factor affecting tourists’ behavior [69
], which emphasizes collecting and using quarterly tourism data in tourism research. However, this study is limited because national urban tourism data sources only have annual scale records. Second, other underlying tourism-related factors such as tourism resources were not fully considered in this study [34
]. Future studies might focus on a relatively small area with seasonal heterogeneity by using multi-source tourism data to construct more scientific indicators [70
] and developing more sophisticated spatiotemporal statistical models for outputting more informative results for regional tourism research.