Assessment of the cultural-natural importance of the scenic spots from a network perspective

自然(考古学) 旅游 栖息地 地理 透视图(图形) 宣传 野生动物走廊 环境资源管理 人口 计算机科学 生态学 环境科学 人工智能 业务 社会学 生物 人口学 考古 营销
作者
Yu Han,Yanxu Liu,Chenxu Wang,Xutong Wu,Kevin Wang
出处
期刊:Landscape and Urban Planning [Elsevier]
卷期号:239: 104861-104861 被引量:9
标识
DOI:10.1016/j.landurbplan.2023.104861
摘要

A scenic spot within a natural landscape is not only a spatial carrier of biodiversity but also an indispensable spatial element of culture. In the lower reaches of large river basins, natural conservation and cultural development are both important. Taking the scenic spots in the lower reach of the Yellow River as study objects, we used minimum cumulative resistance, circuit theory, and igraph package to build a cultural network, natural network and cultural-natural network to assess the importance of scenic spots based on connectivity. The scenic spots were grouped into four categories (based on the relative importance of habitat connectivity and tourism connectivity) and three optimization scenarios (including the cultural, natural, and cultural-natural approaches) were set to indicate the impact of population, resistance and publicity on the importance of scenic spots. We found that important nodes in each of the networks were clustered in different parts of the region. Only 2 of the 18 nodes belonged to the category in which habitat connectivity is not important and tourism connectivity is important, and 7 nodes belonged to the category in which habitat connectivity is not important and tourism connectivity is not important, which is the largest category. The cultural-natural optimization method and the natural optimization method are respectively the best in terms of the degree of the importance of the value of the optimization objects and the number of the importance of optimization objects. The network-based assessment method we provide can support landscape planning from a cultural-natural perspective and provide a potential ranking for landscape connectivity optimization that considers both ecological connectivity and social connectivity.
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