Socioeconomic and Environmental Impacts on Regional Tourism across Chinese Cities: A Spatiotemporal Heterogeneous Perspective

Abstract

Understanding geospatial impacts of multi-sourced drivers on the tourism industry is of great significance for formulating tourism development policies tailored to regional-specific needs. To date, no research in China has explored the combined impacts of socioeconomic and environmental drivers on city-level tourism from a spatiotemporal heterogeneous perspective. We collected the total tourism revenue indicator and 30 potential influencing factors from 343 cities across China during 2008–2017. Three mainstream regressions and an emerging local spatiotemporal regression named the Bayesian spatiotemporally varying coefficients (Bayesian STVC) model were constructed to investigate the global-scale stationary and local-scale spatiotemporal nonstationary relationships between city-level tourism and various vital drivers. The Bayesian STVC model achieved the best model performance. Globally, eight socioeconomic and environmental factors, average wage (coefficient: 0.47, 95% credible intervals: 0.43–0.51), employed population (−0.14, −0.17–−0.11), GDP per capita (0.47, 0.42–0.52), population density (0.21, 0.16–0.27), night-time light index (−0.01, −0.08–0.05), slope (0.10, 0.06–0.14), vegetation index (0.66, 0.63–0.70), and road network density (0.34, 0.29–0.38), were identified to have nonlinear effects on tourism. Temporally, the main drivers might have gradually changed from the local macro-economic level, population density, and natural environment conditions to the individual economic level over the last decade. Spatially, city-specific dynamic maps of tourism development and geographically clustered influencing maps for eight drivers were produced. In 2017, China formed four significant city-level tourism industry clusters (hot spots, 90% confidence), the locations of which coincide with China’s top four urban agglomerations. Our local spatiotemporal analysis framework for geographical tourism data is expected to provide insights into adjusting regional measures to local conditions and temporal variations in broader social and natural sciences.

Spatiotemporal disparities in regional public risk perception of COVID-19 using Bayesian Spatiotemporally Varying Coefficients (STVC) series models across Chinese cities

“…Moreover, through introducing spatial (or temporal) stratified heterogeneity to define the spatiotemporal interaction non-stationarity, the STIVC model not only ensures the proper complexity of the model and the feasibility of Bayesian inference and improves model fit and prediction ability, but also avoids the over-fitting problem of an SLH-level spatiotemporal interaction non-stationary regression [ 50 ]. In contrast, the STVI and STIVI models serve as two simplified versions of the STVC and STIVC models without considering any non-stationary effects of covariates and are thus used only to fit the multiscale spatiotemporal variation towards the target variable [ 58 , 67 ]. For this COVID-19 case, Bayesian STVC series models were successfully used to achieve the spatiotemporal analysis of the description and influencing factors for regional public attention and to further estimate the regional PRPI for advanced cluster and outlier mapping analysis to identify sensitive areas.…”

“…Among the STVC's six posterior parameters , are defined as the space-coefficients (SCs) representing the spatially heterogeneous associations between regional public attention ( Y ) and factors SX across all study locations, are the time-coefficients (TCs) that measure the temporally heterogeneous associations between Y and factors TX during each time frame, are the overall coefficients of control factors CX (no such factors in this case), are the space-intercepts (SIs) representing the spatial pattern of Y , are the time-intercepts (TIs) representing the temporal trend of Y , and is the global intercept. Note that only the local parameters SCs and TCs are two necessary components of an STVC model, while all the other four parameters, including SIs and TIs, are optional [ 50 , 58 , 67 ].…”