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Hotspot Identification and Causal Analysis of Chinese Rural Tourism at Different Spatial and Temporal Scales Based on Tourism Big Data

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  • Yuanfang Fu

    (School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China)

  • Zhenrao Cai

    (School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China)

  • Chaoyang Fang

    (School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China
    Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University, Nanchang 330022, China
    Nanchang Base, International Centre on Space Technologies for Natural and Cultural Heritage (HIST) under the Auspices of UNESCO, Nanchang 330022, China)

Abstract

Rural tourism serves as a crucial means for fostering rural economic prosperity and inheriting rural culture. The assessment of the quality of rural tourism development and the identification of disparities in rural tourism development among regions have become focal points in current research. This paper utilizes tourism big data to establish a system for evaluating rural tourism popularity and proposes a method for identifying rural tourism hotspots. The study explores the spatiotemporal evolution characteristics and formation mechanisms of the cold and hot patterns of rural tourism in China during two periods (pre-pandemic and post-pandemic) and on two spatial scales (provincial and municipal levels). The research findings indicate that (1) the annual variation in rural tourism popularity exhibits a fluctuating upward trend, with significant seasonal variations on a monthly basis. (2) The spatial pattern of rural tourism popularity changes with the scale effect. At the provincial level, hotspot areas form an east–west dual-core pattern, while at the municipal level, hotspot areas demonstrate an evolution from a three-core to a four-core pattern. In the post-pandemic era, rural tourism popularity in the northwest and southwest regions is experiencing a counter-trend growth. (3) At different spatiotemporal scales, influencing factors and their impact intensities vary. At the provincial level, road density and reception capacity consistently play dominant roles, and per capita disposable income significantly influences early-stage popularity enhancement. At the municipal level, resident population and tourism resources influence are the dominant factors, and the influence of air quality and regional media attention gradually strengthens. This article provides a new perspective on quantitative research in rural tourism, offering significant guidance for the rational allocation of resources in rural tourism, regional tourism collaboration, and the sustainable development of rural tourism in the post-pandemic era.

Suggested Citation

  • Yuanfang Fu & Zhenrao Cai & Chaoyang Fang, 2024. "Hotspot Identification and Causal Analysis of Chinese Rural Tourism at Different Spatial and Temporal Scales Based on Tourism Big Data," Sustainability, MDPI, vol. 16(3), pages 1-24, January.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:3:p:1165-:d:1329563
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    References listed on IDEAS

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