The new path of tourism planning development based on MSPA-connectivity-space syntax

Article type: Research Article

Authors: Cao, Yimin

Affiliations: CCDG Urban-Rural Landscape Planning and Design Institute, Beijing 100000, China | E-mail: cao1986163@163.com

Correspondence: [*] Corresponding author: CCDG Urban-Rural Landscape Planning and Design Institute, Beijing 100000, China. E-mail: cao1986163@163.com.

Abstract: As China’s tourism industry is on the right track, the country has gradually paid more attention to the ecological protection of tourism areas. Under the concept of sustainable development, the research on environmental adaptability of tourist attractions has become a hotspot. This study took Huanglongxi Ancient Town in Shuangliu District, Chengdu City, Sichuan Province as the research object, and determined seven ecological protection spaces of Huanglongxi Ancient Town by MSPA method, and then used the landscape connectivity method to identify the priority of ecological sources. The high green space and water are the “source”, and finally the path network is constructed using space syntax, and the relationship between the flow of people and the path resistance disturbance is calculated. After analysis, it is concluded that Huanglongxi Ancient Town has 2 green spaces with higher priority and 1 water area with higher priority. The route layout can meet the current annual reception volume and will not cause obvious congestion during the daily peak. Huanglongxi Ancient Town has 6 enterprises above designated size and 20,000 square kilometers of arable land. The average dLLC of the green space in Huanglongxi Ancient Town is 19.10, the average dPC is 20.92, the maximum time resistance is 0.951 + 1.703*10-7*V151.3, and the maximum time resistance disturbance is 0.999. Huanglongxi Ancient Town can pass between paths 7–8. Add new paths to improve the path situation.

Keywords: MSPA, landscape connectivity, ecological source area, space syntax, path network, environmental adaptability

DOI: 10.3233/JCM-226707

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 23, no. 3, pp. 1321-1333, 2023