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<p>Cultural heritage carries the material and cultural connotations of various historical periods. This study uses Bayesian modelling to assess the drivers on the spatial distribution of cultural heritage in Lushan County. The findings reveal that, first, the hierarchical Bayesian model effectively captures the heterogeneity of the drivers across heritage types and quantifies the variations in their intensities. Second, the spatial distribution shows a “south-dense, north-sparse” pattern. At the overall level, elevation, distance from settlements, and distance from cultural centers have a significant negative effect on heritage density, while distance from geological hazard sites has a significant positive effect. Third, social drivers exert a significantly greater effect than natural drivers, with their effect strengthening over time. Fourth, the significance of the regression coefficients, and the strength and direction of each driver’s effect, vary across different heritage types. These results provide data-driven methodological and theoretical references for cultural heritage research.</p>

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Multiple Bayesian models approach to assessing drivers of cultural heritage spatial distribution illustrated Lushan County in China

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月21日

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  • Yuxi Liu,
  • Xinyu Du,
  • Yu Bai,
  • Qibing Chen,
  • Dong Liu

摘要

Cultural heritage carries the material and cultural connotations of various historical periods. This study uses Bayesian modelling to assess the drivers on the spatial distribution of cultural heritage in Lushan County. The findings reveal that, first, the hierarchical Bayesian model effectively captures the heterogeneity of the drivers across heritage types and quantifies the variations in their intensities. Second, the spatial distribution shows a “south-dense, north-sparse” pattern. At the overall level, elevation, distance from settlements, and distance from cultural centers have a significant negative effect on heritage density, while distance from geological hazard sites has a significant positive effect. Third, social drivers exert a significantly greater effect than natural drivers, with their effect strengthening over time. Fourth, the significance of the regression coefficients, and the strength and direction of each driver’s effect, vary across different heritage types. These results provide data-driven methodological and theoretical references for cultural heritage research.