Analysis Model of the Relationship between Public Spatial Forms in Traditional Villages and Scenic Beauty Preference Based on LiDAR Point Cloud Data

Guodong Chen, Xinyu Sun, Wenbo Yu and Hao Wang
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Guodong Chen: College of Landscape Architecture, Nanjing Forestry University, 159 Longpan Rd., Nanjing 210037, China
Xinyu Sun: College of Forestry, Nanjing Forestry University, 159 Longpan Rd., Nanjing 210037, China
Wenbo Yu: College of Landscape Architecture, Nanjing Forestry University, 159 Longpan Rd., Nanjing 210037, China
Hao Wang: College of Landscape Architecture, Nanjing Forestry University, 159 Longpan Rd., Nanjing 210037, China

Land, 2022, vol. 11, issue 8, 1-21

Abstract: Traditional villages are historically, culturally, scientifically and aesthetically valuable, and a beautiful landscape is the primary embodiment of a traditional village environment. Urbanization and modernization have had a great impact on village landscapes. As an important aspect of traditional village landscapes, creating beautiful public spaces is an effective way to attract tourists and improve the well-being of residents. Landscape aesthetic activities are the result of the interaction between landscape objects and aesthetic subjects. Research on the relationship between the form of traditional village public spaces and subjective aesthetic preferences has long been neglected. This research examined 31 public spaces in traditional villages in the Dongshan and Xishan areas in Lake Taihu, Suzhou. An index system of public spatial forms in traditional villages was created, basic data of spatial forms were collected using a hand-held 3D laser scanner, and the value of the spatial forms index was calculated using R language. The scenic beauty estimation (SBE) method was improved, with the estimation of the beauty of the scenic environment based on VR panorama rather than traditional photo media. Parameter screening was performed using correlation analysis and full subset regression analysis, and four models were used to fit the SBE scores and grades. The results show that the majority of public spaces had lower than average SBE scores, and the four key indicators of average contour upper height, solid-space ratio, vegetation cover, and comprehensive closure predicted SBE. In addition, the linear model (R 2 = 0.332, RMSE = 64.774) had the most accurate SBE level prediction and the stochastic forest model (R 2 = 0.405, RMSE = 63.311) was better at predicting specific SBE scores. The model provides managers, designers, and researchers with a method for the quantitative evaluation of visual landscape preferences and quantitative landscape spatial forms and provides a reference for the protection and renewal of traditional village landscapes.

Keywords: landscape architecture; spatial form; spatial quantification; scenic beauty estimation (SBE); point cloud data (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
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