Spatial Distribution of Ice and Snow Tourism Resource Sites in Xinjiang Region Based on Logarithmic Gaussian Cox Model and Its Influencing Factor Analysis

Ke Li, Chao Xu, Jing Lin and Xijian Hu

Discrete Dynamics in Nature and Society, 2025, vol. 2025, 1-10

Abstract: Xinjiang is rich in snow and ice tourism resources, and exploring the spatial distribution pattern of snow and ice tourism resource points in the region, and the influence mechanism behind it can provide decision support for the region to build a strong snow and ice tourism area. In this paper, we will consider the influence of meteorological factors (including six items of snowfall, air temperature, air pressure, humidity, wind speed, and rainfall), topographical factors (including two items of elevation and slope), and five infrastructures (including catering and food, shopping and consumption, leisure and entertainment, hotel accommodation, and transportation facilities) on the distribution of ice and snow tourism resource points. Under the Bayesian modeling framework, we will use the Integrated Nested Laplace Algorithm (INLA) to fit the Log-Gaussian Cox Process model (LGCP) to analyze the differences in the spatial distribution patterns of this snow and ice resource point and the causes of the impacts. The final results of the study found that (1) the spatial distribution of snow and ice tourism resource sites in the Xinjiang region has obvious aggregation, showing the characteristics of “aggregation in the north and dispersion in the south.†(2) According to the fitting results of the influencing factors in the model, it can be obtained that snowfall, temperature and wind speed in meteorological factors, elevation and slope in topographical factors, catering and food, and hotel accommodation and transportation facilities in infrastructure factors have a significant impact on the distribution of ice and snow tourism resources in Xinjiang. This paper analyzes the characteristics of the distribution of snow and ice tourism resources in the Xinjiang region and the causes behind the analysis and provides targeted policy recommendations.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnddns:4715013

DOI: 10.1155/ddns/4715013

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