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Spatial Analysis of Network Attention on Tourism Resources for Sustainable Tourism Development in Western Hunan, China: A Multi-Source Data Approach

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  • Huizi Zeng

    (School of Architecture, Changsha University of Science & Technology, Changsha 410076, China)

  • Chengjun Tang

    (School of Architecture, Changsha University of Science & Technology, Changsha 410076, China)

  • Chen Zhou

    (School of Architecture, Changsha University of Science & Technology, Changsha 410076, China)

  • Peng Zhou

    (School of Architecture, Changsha University of Science & Technology, Changsha 410076, China)

Abstract

Understanding the tourism resource network attention is crucial for promoting sustainable tourism development. This study utilized multi-source data to assess tourism resource network attention in Western Hunan, with GIS spatial analysis and the Geodetector method applied to identify spatial patterns and influencing factors. The results indicate a distinct “dual-core” spatial clustering in network attention, with natural landscape resources centralized in Zhangjiajie and cultural landscape resources in Xiangxi Prefecture. Recreational tourism resources exhibit a similar clustering pattern around these primary and secondary centers. The factors and intensities influencing network attention differ by tourism resource type. For overall tourism resources, natural landscapes, and cultural landscapes, tourist attractions rating (X 11 ) and attraction clustering degree (X 12 ) are the primary drivers, with the strongest impact on natural landscapes (q = 0.648, 0.373), followed by overall resources (q = 0.361, 0.216) and cultural landscapes (q = 0.311, 0.206). In contrast, recreational resources are most influenced by nearby attractions and tourism service capacity (q(X 12 ) = 0.743, q(X 15 ) = 0.620), alongside notable effects from regional factors related to economic development, industrial structure, and tourism development (X 1 –X 9 ). The interaction between inherent tourism resource characteristics (X 10 –X 15 ) and regional environmental factors (X 1 –X 9 ) enhances the driving effect on tourism resource network attention. These findings inform differentiated, resource-specific tourism planning strategies for sustainable development in Western Hunan, promoting balanced regional growth and optimized resource management through a data-driven approach.

Suggested Citation

  • Huizi Zeng & Chengjun Tang & Chen Zhou & Peng Zhou, 2025. "Spatial Analysis of Network Attention on Tourism Resources for Sustainable Tourism Development in Western Hunan, China: A Multi-Source Data Approach," Sustainability, MDPI, vol. 17(2), pages 1-20, January.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:2:p:744-:d:1570164
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    References listed on IDEAS

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    1. 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.
    2. Ulrich Gunter & Irem Önder & Stefan Gindl, 2019. "Exploring the predictive ability of LIKES of posts on the Facebook pages of four major city DMOs in Austria," Tourism Economics, , vol. 25(3), pages 375-401, May.
    3. Almudena Nolasco-Cirugeda & Clara García-Mayor & Cristina Lupu & Alvaro Bernabeu-Bautista, 2022. "Scoping out urban areas of tourist interest though geolocated social media data: Bucharest as a case study," Information Technology & Tourism, Springer, vol. 24(3), pages 361-387, September.
    4. Tiansong Zhu & Kaiping Yu & Bo Wang, 2023. "Spatial Distribution Characteristics and Influencing Factors of Cultural and Tourism Resources in Xihu District of Hangzhou," Sustainability, MDPI, vol. 15(14), pages 1-20, July.
    5. Hernández, Juan M. & Kirilenko, Andrei P. & Stepchenkova, Svetlana, 2018. "Network approach to tourist segmentation via user generated content," Annals of Tourism Research, Elsevier, vol. 73(C), pages 35-47.
    6. Zhang, Ziqiong & Zhang, Zili & Yang, Yang, 2016. "The power of expert identity: How website-recognized expert reviews influence travelers' online rating behavior," Tourism Management, Elsevier, vol. 55(C), pages 15-24.
    7. Bigne, Enrique & Ruiz, Carla & Curras-Perez, Rafael, 2019. "Destination appeal through digitalized comments," Journal of Business Research, Elsevier, vol. 101(C), pages 447-453.
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