Communications in Humanities Research ISSN: 2753-7072
Home> Volumes> Volume CHR Vol.113> Article
Research Article

Spatial Pattern and Planning Response of the County-Level Cultural Tourism Industry in Shanxi Province Based on Multi-Source Data

Ensu LiuShanxi University of Finance and Economics*

* Corresponding author: [email protected]

Abstract

Taking the theory of six elements of tourism as the research framework, this paper adopts multiple methods including the nearest neighbor index, kernel density analysis, bivariate spatial autocorrelation model and Ripley's K function. Based on the point of interest (POI) dataset, it analyzes the spatial distribution of cultural tourism industry and the correlation between various elements, and discusses planning response strategies combined with spatial risk identification. The results show that: (1) The county-level cultural tourism industry in Shanxi Province presents an extremely significant agglomeration distribution on the whole. The agglomeration degree from high to low is ranked as Shopping > Sightseeing > Catering > Accommodation > Entertainment > Transportation, forming a dual agglomeration mode of city dependence and resource orientation. (2) The six tourism elements have weak capacity for continuous spatial expansion, presenting a hierarchical layout of "one primary center, one secondary center and multiple supporting nodes". At the micro county level, the overall global spatial coupling correlation of the six elements is relatively low. The Transportation element plays a prominent role in connecting spatial corridors, while the spatial matching degree between Sightseeing and supporting service elements is low, leading to insufficient coordination among all elements. (3) The six tourism elements show obvious spatial scale differentiation. Service-oriented elements rapidly agglomerate and decline on small and medium scales, while resource-oriented elements continuously expand on large scales. The Accommodation element maintains stable agglomeration across all scales and possesses the strongest spatial resilience. (4) Aiming at the problems of spatial imbalance, element mismatch and insufficient coordination, this study proposes to build a spatial network development system of "one core, three corridors and multiple nodes". Combined with corridor connection, supporting facility improvement for scenic spots, transportation hub upgrading and differentiated development of counties, relevant planning response strategies are put forward.

Keywords: Cultural tourism industry; Spatial pattern; POI; Planning response; Shanxi Province
Published: June 23, 2026
DOI: 10.54254/2753-7064/2026.HT34718
Volume: CHR Vol.113
pp. 34-51
Download PDF

References

  1. Hansen, W. G. (1959). How accessibility shapes land use. Journal of the American Institute of Planners, 25(2), 73-76.
  2. Jiang, Y. H., Huang, W., Xiong, X. X., et al. (2024). Investigating spatial patterns and determinants of tourist attractions utilizing POI data: A case study of Hubei Province, China. Heliyon, 10(11), e32370.
  3. Sun, F. Z., Xu, M. Z., Li, Z. H., et al. (2024). Spatial distribution, accessibility, and influencing factors of the tourism and leisure industry in Qingdao, China. Sustainability, 16(16), 6961.
  4. Pan, J., & Cong, Y. (2012). Spatial accessibility measurement of 4A-level and above tourist attractions (spots) in China. Scientia Geographica Sinica, 32(11), 1321-1327.
  5. Ge, D., Zheng, Y., & Tong, L. (2023). Spatial differentiation and influencing factors of rural tourism industry in Zhejiang Province based on POI data mining. Journal of Zhejiang University (Science Edition), 50(4), 483-494.
  6. Wan, H., Wang, X., Huang, M., et al. (2024). Spatial pattern and influencing factors of county-level tourism elements in Guanzhong Plain urban agglomeration based on POI data. Arid Land Resources and Environment, 38(6), 200-208.
  7. Tian, Z., Yu, Y., & Huang, C. (2025). Spatial distribution characteristics and influencing factors of tourism elements in Inner Mongolia counties based on POI data mining. Arid Land Geography.
  8. Xia, Z., Xia, Y., Wang, N., et al. (2025). Spatial pattern and influencing factors of tourism elements in the urban agglomeration on the northern slope of the Tianshan Mountains based on POI data. Arid Land Geography, 48(7), 1243-1254.
  9. Zhang, S. R., Zhang, T. Y., Ju, H. R., et al. (2024). Enhancing transboundary natural tourism resources governance: Unveiling the spatial pattern and its influencing factors. Journal of Mountain Science, 21(3), 973-986.
  10. Chen, H., Yang, D., Xu, H., et al. (2020). Spatiotemporal pattern evolution of accommodation industry and its spatial correlation with tourist attractions based on POI data. Arid Land Geography, 43(5), 1382-1390.
  11. Wang, K., & Zhou, J. (2024). Synergistic evolution and mechanism of manufacturing and producer services in the Beijing-Tianjin-Hebei region. Journal of Management, 21(9), 1302-1311.
  12. Liu, M., & Hao, W. (2020). Influencing factors of spatial distribution of national A-level tourist attractions in Shanxi Province. Acta Geographica Sinica, 75(4), 878-888.
  13. Wang, J., Li, Y., & Chen, Z. (2022). Spatial distribution characteristics and influencing factors of agritainment in Beijing based on POI data mining. Research on Agricultural Modernization, 43(2), 340-350.
  14. Zhang, J., & Ma, X. (2021). Spatial differentiation and influencing factors of rural tourism destinations in Hunan Province: A case study of five-star rural tourism areas. Journal of Natural Resources, 36(4), 879-892.
  15. Ma, B., Chen, X., Ma, K., et al. (2020). Spatial distribution, type structure and influencing factors of key rural tourism villages in China. Economic Geography, 40(7), 190-199.
  16. Wang, X., & Li, X. (2022). Spatial characteristics and influencing factors of key rural tourism villages in China. Acta Geographica Sinica, 77(4), 900-917.
  17. Wang, S., He, Z., & Guo, Y. (2019). Spatial differentiation and influencing factors of rural tourism model villages in the five northwestern provinces of the Silk Road Economic Belt. Economic Geography, 39(4), 199-206.