• 交通地理 •

### 秦巴山区乡村交通环境脆弱性及影响因素——以陕西省洛南县为例

1. 1. 西北大学城市与环境学院,西安 710127
2. 西安外国语大学旅游学院·人文地理研究所,西安 710128;
• 收稿日期:2018-03-30 修回日期:2019-03-02 出版日期:2019-06-25 发布日期:2019-06-20
• 通讯作者: 高岩辉 E-mail:rwdl_gyh@163.com
• 作者简介:杨晴青(1992-), 女, 湖南益阳人, 博士生, 研究方向为人居环境与区域可持续发展。E-mail: yqq@mails.ccnu.edu.cn
• 基金资助:
国家自然科学基金项目(41571163);西北大学研究生自主创新资助项目(YZZ17149)

### Vulnerability and influencing factors of rural transportation environment in Qinling-Daba mountainous areas: A case study of Luonan county in Shaanxi province

YANG Qingqing1,LIU Qian1,YIN Sha1,ZHANG Jian1,YANG Xinjun1,GAO Yanhui2()

1. 1. College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China
2. School of Tourism & Research Institute of Human Geography, Xi'an International Studies University, Xi'an 710128, China;
• Received:2018-03-30 Revised:2019-03-02 Online:2019-06-25 Published:2019-06-20
• Contact: GAO Yanhui E-mail:rwdl_gyh@163.com
• Supported by:
National Natural Science Foundation of China(41571163);Northwest University Graduate Innovation and Creativity Funds(YZZ17149)

Abstract:

Villages in mountainous areas are under the risk of topography, geomorphology and frequent natural disasters in a long term. Rural transportation system is characterized by low network degree and weak capacity to resist disasters, and the problem of vulnerability of traffic environment is prominent. Taking Luonan county in the Qinling-Daba mountainous areas as an example and based on exposure, sensitivity and response capacity of human-environment system vulnerability, this paper constructed a basic framework of rural transportation environment vulnerability, which contained the key elements of risk events, geographical features, key travel path, road network structures, public and private vehicles, family capital etc., and established a targeted evaluation index system. With the aid of ArcGIS and GeoDa, this research examined the spatial structure and spatial autocorrelation of the transportation environment vulnerability in Luonan county at the village level. It also utilized a geographical weighted regression model to explore the factors of natural conditions, population, socio-economic development, which had influence on response capacity of traffic risk and its spatial difference. The results showed that the vulnerability of transportation environment took the county seat and the suburbs as the center increasing outward, which presents a circle structure featured by great difference in vertical direction. Simultaneously, the vulnerability of the transportation environment in village-level residential areas showed a significant positive spatial autocorrelation, but both exposure degree and sensitivity showed a significant spatial negative correlation with the response capacity. There were three patterns of local spatial correlation in transportation environment vulnerability: the vulnerability of the local "hot spots" areas was widely observed in the north- central mountainous area and fell into the dilemma of high exposure, high sensitivity and low response capacity, while the "cold spots" villages were founded in suburbs or areas adjacent to the towns with higher income, which had low sensitivity and high response capacity. There were a few "heterogeneity points", and these villages were adjacent to low-vulnerable villages, but they belonged to high-vulnerable areas. Moreover, topographical condition, industrial distribution, population structure, education level and family size had a significant impact on response capacity of transportation risk. In addition, the effect direction and intensity of the influencing factors had significant spatial differences.