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The evaluation of tourism development potential (TDP) is the crucial foundation and critical step for sustainable regional tourism development. Prior studies mainly evaluate TDP through the univariate potential model and the multi-indicator descriptive evaluation. However, these two methods have only limited effectiveness for the destination’s TDP in the context of the mesoscale level. Thus, this study aims to develop an effective multi-dimensional mesoscale to evaluate the destination’s TDP and construct a potential index model. Based on the literature review, this study develops four rule layers (tourism supply and consumption (X1), the demand and purchasing power of tourist source (X2), development value of destination resources (X3), and the contribution of the destination’s tourism industry (X4)) and 31 factor layers. All the factor layers are then assigned values based on the provincial statistics in China in 2019. Through SPSS 24.0, the current study uses the principal component analysis (PCA) to construct a provincial TDP index model for the research area: Y=0.2573X1+0.1305X2+0.3177X3+0.2945X4. The results show significant regional differences in the TDP index of the provinces along the Belt and Road (study area) in China. Among them, Guangdong has the most extensive TDP index, Qinghai has the smallest TDP index. The study also uses ArcGIS 10.2 for the function of kernel density analysis to visualize provincial TDP and finds significant spatial differences and a central-edge distribution pattern across provinces.
Chen et al. (Fri,) studied this question.
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